Changeset 368 for doc/html/libEF_8h-source.html

Timestamp:

06/08/09 18:02:55 (16 years ago)

Author:

smidl

Message:

doc

Files:

• doc/html/libEF_8h-source.html (modified) (2 diffs)

doc/html/libEF_8h-source.html

@@ -70 +70 @@
 <a name="l00017"></a>00017 <span class="preprocessor">#include "<a class="code" href="libBM_8h.html" title="Bayesian Models (bm) that use Bayes rule to learn from observations.">libBM.h</a>"</span>
 <a name="l00018"></a>00018 <span class="preprocessor">#include "../math/chmat.h"</span>
-<a name="l00019"></a>00019 <span class="comment">//#include &lt;std&gt;</span>
+<a name="l00019"></a>00019 <span class="comment">//#include "../user_info.h"</span>
 <a name="l00020"></a>00020 
 <a name="l00021"></a>00021 <span class="keyword">namespace </span>bdm
@@ -412 +412 @@
 <a name="l00515"></a><a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564">00515</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a> ( <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g0, <span class="keyword">const</span> sq_T &amp;R0 ) {g=g0; <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( g-&gt;<a class="code" href="classbdm_1_1fnc.html#083832294da9d1e40804158b979c4341" title="access function">dimension</a>() ), R0 );}
 <a name="l00516"></a><a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d">00516</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#b31d63472cf6a1030cd8dbd8094c1f6d" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;cond ) {mu=g-&gt;<a class="code" href="classbdm_1_1fnc.html#6277b11d7fffc7ef8a2fa3e84ae5bad4" title="function evaluates numerical value of  at  cond ">eval</a> ( cond );};
-<a name="l00517"></a>00517         };
+<a name="l00517"></a>00517 
 <a name="l00518"></a>00518 
-<a name="l00526"></a><a class="code" href="classbdm_1_1mlstudent.html">00526</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt;
-<a name="l00527"></a>00527         {
-<a name="l00528"></a>00528                 <span class="keyword">protected</span>:
-<a name="l00529"></a>00529                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
-<a name="l00530"></a>00530                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
-<a name="l00531"></a>00531                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
-<a name="l00532"></a>00532                 <span class="keyword">public</span>:
-<a name="l00533"></a>00533                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
-<a name="l00534"></a>00534                                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
-<a name="l00535"></a>00535                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 )
-<a name="l00536"></a>00536                         {
-<a name="l00537"></a>00537                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l00538"></a>00538                                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
-<a name="l00539"></a>00539 
-<a name="l00540"></a>00540                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
-<a name="l00541"></a>00541                                 A = A0;
-<a name="l00542"></a>00542                                 mu_const = mu0;
-<a name="l00543"></a>00543                                 Re=R0;
-<a name="l00544"></a>00544                                 Lambda = Lambda0;
-<a name="l00545"></a>00545                         }
-<a name="l00546"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00546</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &amp;cond )
-<a name="l00547"></a>00547                         {
-<a name="l00548"></a>00548                                 _mu = A*cond + mu_const;
-<a name="l00549"></a>00549                                 <span class="keywordtype">double</span> zeta;
-<a name="l00550"></a>00550                                 <span class="comment">//ugly hack!</span>
-<a name="l00551"></a>00551                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
+<a name="l00546"></a><a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">00546</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgnorm.html#d23d2c9b147c01785d5a4239c118ddf1">from_setting</a>( <span class="keyword">const</span> Setting &amp;root ) 
+<a name="l00547"></a>00547                         {       
+<a name="l00548"></a>00548                                 <a class="code" href="classbdm_1_1fnc.html" title="Class representing function  of variable  represented by rv.">fnc</a>* g = UI::build&lt;fnc&gt;( root, <span class="stringliteral">"g"</span> );
+<a name="l00549"></a>00549 
+<a name="l00550"></a>00550                                 mat R;
+<a name="l00551"></a>00551                                 <span class="keywordflow">if</span> ( root.exists( <span class="stringliteral">"dR"</span> ) )
 <a name="l00552"></a>00552                                 {
-<a name="l00553"></a>00553                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
-<a name="l00554"></a>00554                                 }
-<a name="l00555"></a>00555                                 <span class="keywordflow">else</span>
-<a name="l00556"></a>00556                                 {
-<a name="l00557"></a>00557                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
-<a name="l00558"></a>00558                                 }
-<a name="l00559"></a>00559                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
-<a name="l00560"></a>00560                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
-<a name="l00561"></a>00561                         };
+<a name="l00553"></a>00553                                         vec dR;
+<a name="l00554"></a>00554                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( dR, root, <span class="stringliteral">"dR"</span> );
+<a name="l00555"></a>00555                                         R=diag(dR);
+<a name="l00556"></a>00556                                 }
+<a name="l00557"></a>00557                                 <span class="keywordflow">else</span> 
+<a name="l00558"></a>00558                                         <a class="code" href="classbdm_1_1UI.html#652bfd23f5052e4f1cb317057d74a3e2" title="This methods tries to build a new double matrix.">UI::get</a>( R, root, <span class="stringliteral">"R"</span>);                                 
+<a name="l00559"></a>00559                 
+<a name="l00560"></a>00560                                 <a class="code" href="classbdm_1_1mgnorm.html#578e02458e2a0d17f3864826b6ebd564" title="set mean function">set_parameters</a>(g,R);
+<a name="l00561"></a>00561                         }
 <a name="l00562"></a>00562 
-<a name="l00563"></a>00563         };
-<a name="l00573"></a><a class="code" href="classbdm_1_1mgamma.html">00573</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00574"></a>00574         {
-<a name="l00575"></a>00575                 <span class="keyword">protected</span>:
-<a name="l00577"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00577</a>                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00579"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00579</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
-<a name="l00581"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00581</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
-<a name="l00582"></a>00582 
-<a name="l00583"></a>00583                 <span class="keyword">public</span>:
-<a name="l00585"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00585</a>                         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00587"></a>00587                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &amp;beta0 );
-<a name="l00588"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00588</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
-<a name="l00589"></a>00589         };
-<a name="l00590"></a>00590 
-<a name="l00600"></a><a class="code" href="classbdm_1_1migamma.html">00600</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
-<a name="l00601"></a>00601         {
-<a name="l00602"></a>00602                 <span class="keyword">protected</span>:
-<a name="l00604"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00604</a>                         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
-<a name="l00606"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00606</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
-<a name="l00608"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00608</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
-<a name="l00610"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00610</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
-<a name="l00611"></a>00611 
-<a name="l00612"></a>00612                 <span class="keyword">public</span>:
-<a name="l00615"></a>00615                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00616"></a>00616                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
-<a name="l00618"></a>00618 
-<a name="l00620"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00620</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
-<a name="l00621"></a>00621                         {
-<a name="l00622"></a>00622                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
-<a name="l00623"></a>00623                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
-<a name="l00624"></a>00624                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00625"></a>00625                         };
-<a name="l00626"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00626</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00627"></a>00627                         {
-<a name="l00628"></a>00628                                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
-<a name="l00629"></a>00629                         };
-<a name="l00630"></a>00630         };
-<a name="l00631"></a>00631 
-<a name="l00643"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00643</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a>
-<a name="l00644"></a>00644         {
-<a name="l00645"></a>00645                 <span class="keyword">protected</span>:
-<a name="l00647"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00647</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
-<a name="l00649"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00649</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
-<a name="l00650"></a>00650                 <span class="keyword">public</span>:
-<a name="l00652"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00652</a>                         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
-<a name="l00654"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00654</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
-<a name="l00655"></a>00655                         {
-<a name="l00656"></a>00656                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
-<a name="l00657"></a>00657                                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
-<a name="l00658"></a>00658                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
-<a name="l00659"></a>00659                         };
-<a name="l00660"></a>00660 
-<a name="l00661"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00661</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
-<a name="l00662"></a>00662         };
-<a name="l00663"></a>00663 
-<a name="l00664"></a>00664 
-<a name="l00677"></a><a class="code" href="classbdm_1_1migamma__ref.html">00677</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
-<a name="l00678"></a>00678         {
-<a name="l00679"></a>00679                 <span class="keyword">protected</span>:
-<a name="l00681"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00681</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
-<a name="l00683"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00683</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
-<a name="l00684"></a>00684                 <span class="keyword">public</span>:
-<a name="l00686"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00686</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
-<a name="l00688"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00688</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
-<a name="l00689"></a>00689                         {
-<a name="l00690"></a>00690                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
-<a name="l00691"></a>00691                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
-<a name="l00692"></a>00692                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
-<a name="l00693"></a>00693                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
-<a name="l00694"></a>00694                         };
-<a name="l00695"></a>00695 
-<a name="l00696"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00696</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00697"></a>00697                         {
-<a name="l00698"></a>00698                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
-<a name="l00699"></a>00699                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
-<a name="l00700"></a>00700                         };
-<a name="l00701"></a>00701         };
-<a name="l00702"></a>00702 
-<a name="l00712"></a><a class="code" href="classbdm_1_1elognorm.html">00712</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
-<a name="l00713"></a>00713         {
-<a name="l00714"></a>00714                 <span class="keyword">public</span>:
-<a name="l00715"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00715</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
-<a name="l00716"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00716</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
-<a name="l00717"></a>00717 
-<a name="l00718"></a>00718         };
-<a name="l00719"></a>00719 
-<a name="l00731"></a><a class="code" href="classbdm_1_1mlognorm.html">00731</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
-<a name="l00732"></a>00732         {
-<a name="l00733"></a>00733                 <span class="keyword">protected</span>:
-<a name="l00734"></a>00734                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
-<a name="l00736"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00736</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
-<a name="l00738"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00738</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
-<a name="l00739"></a>00739                 <span class="keyword">public</span>:
-<a name="l00741"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00741</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
-<a name="l00743"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00743</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
-<a name="l00744"></a>00744                         {
-<a name="l00745"></a>00745                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
-<a name="l00746"></a>00746                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
-<a name="l00747"></a>00747 
-<a name="l00748"></a>00748                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
-<a name="l00749"></a>00749                         };
-<a name="l00750"></a>00750 
-<a name="l00751"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00751</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
-<a name="l00752"></a>00752                         {
-<a name="l00753"></a>00753                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
+<a name="l00563"></a>00563                         <span class="comment">/*void mgnorm::to_setting( Setting &amp;root ) const</span>
+<a name="l00564"></a>00564 <span class="comment">                        {       </span>
+<a name="l00565"></a>00565 <span class="comment">                                Transport::to_setting( root );</span>
+<a name="l00566"></a>00566 <span class="comment"></span>
+<a name="l00567"></a>00567 <span class="comment">                                Setting &amp;kilometers_setting = root.add("kilometers", Setting::TypeInt );</span>
+<a name="l00568"></a>00568 <span class="comment">                                kilometers_setting = kilometers;</span>
+<a name="l00569"></a>00569 <span class="comment"></span>
+<a name="l00570"></a>00570 <span class="comment">                                UI::save( passengers, root, "passengers" );</span>
+<a name="l00571"></a>00571 <span class="comment">                        }*/</span>
+<a name="l00572"></a>00572 
+<a name="l00573"></a>00573         };
+<a name="l00574"></a>00574 
+<a name="l00575"></a>00575         UIREGISTER(mgnorm&lt;chmat&gt;);
+<a name="l00576"></a>00576 
+<a name="l00577"></a>00577 
+<a name="l00585"></a><a class="code" href="classbdm_1_1mlstudent.html">00585</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm</a>&lt;ldmat&gt;
+<a name="l00586"></a>00586         {
+<a name="l00587"></a>00587                 <span class="keyword">protected</span>:
+<a name="l00588"></a>00588                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Lambda;
+<a name="l00589"></a>00589                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;<a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>;
+<a name="l00590"></a>00590                         <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> Re;
+<a name="l00591"></a>00591                 <span class="keyword">public</span>:
+<a name="l00592"></a>00592                         <a class="code" href="classbdm_1_1mlstudent.html">mlstudent</a> ( ) :<a class="code" href="classbdm_1_1mlnorm.html">mlnorm&lt;ldmat&gt;</a> (),
+<a name="l00593"></a>00593                                         Lambda (),      <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>._R() ) {}
+<a name="l00594"></a>00594                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a> &amp;R0, <span class="keyword">const</span> <a class="code" href="classldmat.html" title="Matrix stored in LD form, (commonly known as UD).">ldmat</a>&amp; Lambda0 )
+<a name="l00595"></a>00595                         {
+<a name="l00596"></a>00596                                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l00597"></a>00597                                 it_assert_debug ( R0.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() ==A0.rows(),<span class="stringliteral">""</span> );
+<a name="l00598"></a>00598 
+<a name="l00599"></a>00599                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( mu0,Lambda ); <span class="comment">//</span>
+<a name="l00600"></a>00600                                 A = A0;
+<a name="l00601"></a>00601                                 mu_const = mu0;
+<a name="l00602"></a>00602                                 Re=R0;
+<a name="l00603"></a>00603                                 Lambda = Lambda0;
+<a name="l00604"></a>00604                         }
+<a name="l00605"></a><a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">00605</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlstudent.html#efd37560585c8613897f30d3c2f58d0d">condition</a> ( <span class="keyword">const</span> vec &amp;cond )
+<a name="l00606"></a>00606                         {
+<a name="l00607"></a>00607                                 _mu = A*cond + mu_const;
+<a name="l00608"></a>00608                                 <span class="keywordtype">double</span> zeta;
+<a name="l00609"></a>00609                                 <span class="comment">//ugly hack!</span>
+<a name="l00610"></a>00610                                 <span class="keywordflow">if</span> ( ( cond.length() +1 ) ==Lambda.<a class="code" href="group__math.html#g96dfb21865db4f5bd36fa70f9b0b1163" title="access function">rows</a>() )
+<a name="l00611"></a>00611                                 {
+<a name="l00612"></a>00612                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( concat ( cond, vec_1 ( 1.0 ) ) );
+<a name="l00613"></a>00613                                 }
+<a name="l00614"></a>00614                                 <span class="keywordflow">else</span>
+<a name="l00615"></a>00615                                 {
+<a name="l00616"></a>00616                                         zeta = Lambda.<a class="code" href="classldmat.html#d876c5f83e02b3e809b35c9de5068f14" title="Evaluates quadratic form ;.">invqform</a> ( cond );
+<a name="l00617"></a>00617                                 }
+<a name="l00618"></a>00618                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a> = Re;
+<a name="l00619"></a>00619                                 <a class="code" href="classbdm_1_1mlnorm.html#78120ecd1c2b1d7e80124b4603504604" title="access function">_R</a>*= ( 1+zeta );<span class="comment">// / ( nu ); &lt;&lt; nu is in Re!!!!!!</span>
+<a name="l00620"></a>00620                         };
+<a name="l00621"></a>00621 
+<a name="l00622"></a>00622         };
+<a name="l00632"></a><a class="code" href="classbdm_1_1mgamma.html">00632</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00633"></a>00633         {
+<a name="l00634"></a>00634                 <span class="keyword">protected</span>:
+<a name="l00636"></a><a class="code" href="classbdm_1_1mgamma.html#bdc9f1e9e03c09e91103fee269864438">00636</a>                         <a class="code" href="classbdm_1_1egamma.html" title="Gamma posterior density.">egamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00638"></a><a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09">00638</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>;
+<a name="l00640"></a><a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312">00640</a>                         vec &amp;<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>;
+<a name="l00641"></a>00641 
+<a name="l00642"></a>00642                 <span class="keyword">public</span>:
+<a name="l00644"></a><a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1">00644</a>                         <a class="code" href="classbdm_1_1mgamma.html#1a9dc8661e5b214a8185d6e6b9956eb1" title="Constructor.">mgamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> ( ), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> (), <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00646"></a>00646                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#a0f21c2557b233a85838b497d040ab14" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>, <span class="keyword">const</span> vec &amp;beta0 );
+<a name="l00647"></a><a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff">00647</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma.html#8996500f1885e39cde30221b20900bff" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {<a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/val;};
+<a name="l00648"></a>00648         };
+<a name="l00649"></a>00649 
+<a name="l00659"></a><a class="code" href="classbdm_1_1migamma.html">00659</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a>
+<a name="l00660"></a>00660         {
+<a name="l00661"></a>00661                 <span class="keyword">protected</span>:
+<a name="l00663"></a><a class="code" href="classbdm_1_1migamma.html#a31b39d4179551b593c9e0d7d756783a">00663</a>                         <a class="code" href="classbdm_1_1eigamma.html" title="Inverse-Gamma posterior density.">eigamma</a> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;
+<a name="l00665"></a><a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c">00665</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>;
+<a name="l00667"></a><a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc">00667</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>;
+<a name="l00669"></a><a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5">00669</a>                         vec &amp;<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>;
+<a name="l00670"></a>00670 
+<a name="l00671"></a>00671                 <span class="keyword">public</span>:
+<a name="l00674"></a>00674                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00675"></a>00675                         <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> &amp;m ) : <a class="code" href="classbdm_1_1mEF.html" title="Exponential family model.">mEF</a> (), <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( m.<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ), <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>() ), <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a> ( <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.<a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>;};
+<a name="l00677"></a>00677 
+<a name="l00679"></a><a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151">00679</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#8b10ab922e2a7bae2fb6bb3efc7b6151" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> len, <span class="keywordtype">double</span> k0 )
+<a name="l00680"></a>00680                         {
+<a name="l00681"></a>00681                                 <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>=k0;
+<a name="l00682"></a>00682                                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( ( 1.0/ ( <a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a>*<a class="code" href="classbdm_1_1migamma.html#dc56bc9da542e0103ec16b9be8e5e38c" title="Constant .">k</a> ) +2.0 ) *ones ( len ) <span class="comment">/*alpha*/</span>, ones ( len ) <span class="comment">/*beta*/</span> );
+<a name="l00683"></a>00683                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
+<a name="l00684"></a>00684                         };
+<a name="l00685"></a><a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c">00685</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma.html#7a34b1e2e3aa2250d7c0ed7df1665b8c" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00686"></a>00686                         {
+<a name="l00687"></a>00687                                 <a class="code" href="classbdm_1_1migamma.html#0d854c047001b5465cf1ba21f52904b5" title="cache of epdf.beta">_beta</a>=elem_mult ( val, ( <a class="code" href="classbdm_1_1migamma.html#c9847093da59a9ba0ebb68d2c592f5dc" title="cache of epdf.alpha">_alpha</a>-1.0 ) );
+<a name="l00688"></a>00688                         };
+<a name="l00689"></a>00689         };
+<a name="l00690"></a>00690 
+<a name="l00691"></a>00691 
+<a name="l00703"></a><a class="code" href="classbdm_1_1mgamma__fix.html">00703</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mgamma__fix.html" title="Gamma random walk around a fixed point.">mgamma_fix</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a>
+<a name="l00704"></a>00704         {
+<a name="l00705"></a>00705                 <span class="keyword">protected</span>:
+<a name="l00707"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa">00707</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>;
+<a name="l00709"></a><a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2">00709</a>                         vec <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>;
+<a name="l00710"></a>00710                 <span class="keyword">public</span>:
+<a name="l00712"></a><a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d">00712</a>                         <a class="code" href="classbdm_1_1mgamma__fix.html#9a31bc9b4b60188a18a2a6b588dc4b2d" title="Constructor.">mgamma_fix</a> ( ) : <a class="code" href="classbdm_1_1mgamma.html" title="Gamma random walk.">mgamma</a> ( ),<a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a> () {};
+<a name="l00714"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2">00714</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
+<a name="l00715"></a>00715                         {
+<a name="l00716"></a>00716                                 <a class="code" href="classbdm_1_1mgamma__fix.html#1bfd30e90db9dc1fbda4a9fbb0b716b2" title="Set value of k.">mgamma::set_parameters</a> ( k0, ref0 );
+<a name="l00717"></a>00717                                 <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a>=l0;
+<a name="l00718"></a>00718                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=dimension();
+<a name="l00719"></a>00719                         };
+<a name="l00720"></a>00720 
+<a name="l00721"></a><a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7">00721</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mgamma__fix.html#1d539591deb7a38bb3403c2b396c8ff7" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val ) {vec mean=elem_mult ( <a class="code" href="classbdm_1_1mgamma__fix.html#018c6f901a04e419455308a07eb3b0b2" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1mgamma__fix.html#1eb701506aabb2e6af007e487212d6fa" title="parameter l">l</a> ) ); <a class="code" href="classbdm_1_1mgamma.html#3d95f4dde9214ff6dba265e18af60312" title="cache of epdf.beta">_beta</a>=<a class="code" href="classbdm_1_1mgamma.html#b20cf88cca1fe9b0b8f2a412608bfd09" title="Constant .">k</a>/mean;};
+<a name="l00722"></a>00722         };
+<a name="l00723"></a>00723 
+<a name="l00724"></a>00724 
+<a name="l00737"></a><a class="code" href="classbdm_1_1migamma__ref.html">00737</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1migamma__ref.html" title="Inverse-Gamma random walk around a fixed point.">migamma_ref</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a>
+<a name="l00738"></a>00738         {
+<a name="l00739"></a>00739                 <span class="keyword">protected</span>:
+<a name="l00741"></a><a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d">00741</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>;
+<a name="l00743"></a><a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6">00743</a>                         vec <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>;
+<a name="l00744"></a>00744                 <span class="keyword">public</span>:
+<a name="l00746"></a><a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f">00746</a>                         <a class="code" href="classbdm_1_1migamma__ref.html#f45b15a10f084991ba6b48295f10421f" title="Constructor.">migamma_ref</a> ( ) : <a class="code" href="classbdm_1_1migamma.html" title="Inverse-Gamma random walk.">migamma</a> (),<a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a> ( ) {};
+<a name="l00748"></a><a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800">00748</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">double</span> k0 , vec ref0, <span class="keywordtype">double</span> l0 )
+<a name="l00749"></a>00749                         {
+<a name="l00750"></a>00750                                 <a class="code" href="classbdm_1_1migamma__ref.html#b0b4eb278ef5d0831ec4954ba7bd2800" title="Set value of k.">migamma::set_parameters</a> ( ref0.length(), k0 );
+<a name="l00751"></a>00751                                 <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>=pow ( ref0,1.0-l0 );
+<a name="l00752"></a>00752                                 <a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a>=l0;
+<a name="l00753"></a>00753                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = dimension();
 <a name="l00754"></a>00754                         };
-<a name="l00755"></a>00755         };
-<a name="l00756"></a>00756 
-<a name="l00760"></a><a class="code" href="classbdm_1_1eWishartCh.html">00760</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
-<a name="l00761"></a>00761         {
-<a name="l00762"></a>00762                 <span class="keyword">protected</span>:
-<a name="l00764"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00764</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
-<a name="l00766"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00766</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
-<a name="l00768"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00768</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
-<a name="l00769"></a>00769                 <span class="keyword">public</span>:
-<a name="l00770"></a>00770                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
-<a name="l00771"></a>00771                         mat sample_mat()<span class="keyword"> const</span>
-<a name="l00772"></a>00772 <span class="keyword">                        </span>{
-<a name="l00773"></a>00773                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
-<a name="l00774"></a>00774 
-<a name="l00775"></a>00775                                 <span class="comment">//sample diagonal</span>
-<a name="l00776"></a>00776                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
-<a name="l00777"></a>00777                                 {
-<a name="l00778"></a>00778                                         GamRNG.<a class="code" href="classitpp_1_1Gamma__RNG.html#dfaae19411e39aa87e1f72e409b6babe" title="Set lambda.">setup</a> ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
-<a name="l00779"></a>00779 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00780"></a>00780 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
-<a name="l00781"></a>00781                                 }
-<a name="l00782"></a>00782                                 <span class="comment">//do the rest</span>
-<a name="l00783"></a>00783                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
-<a name="l00784"></a>00784                                 {
-<a name="l00785"></a>00785                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
-<a name="l00786"></a>00786                                         {
-<a name="l00787"></a>00787 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00788"></a>00788 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
-<a name="l00789"></a>00789                                         }
-<a name="l00790"></a>00790                                 }
-<a name="l00791"></a>00791                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
-<a name="l00792"></a>00792                         }
-<a name="l00793"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00793</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
-<a name="l00794"></a>00794 <span class="keyword">                        </span>{
-<a name="l00795"></a>00795                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
-<a name="l00796"></a>00796                         }
-<a name="l00798"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00798</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
-<a name="l00800"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00800</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
-<a name="l00802"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00802</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
-<a name="l00803"></a>00803         };
+<a name="l00755"></a>00755 
+<a name="l00756"></a><a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a">00756</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00757"></a>00757                         {
+<a name="l00758"></a>00758                                 vec mean=elem_mult ( <a class="code" href="classbdm_1_1migamma__ref.html#3692dc67caf4367e15564d37f45476f6" title="reference vector">refl</a>,pow ( val,<a class="code" href="classbdm_1_1migamma__ref.html#cdc1345ba8375fbdb18a69322d2f841d" title="parameter l">l</a> ) );
+<a name="l00759"></a>00759                                 <a class="code" href="classbdm_1_1migamma__ref.html#ae86b2e4ff963d62e05d4e130514634a" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">migamma::condition</a> ( mean );
+<a name="l00760"></a>00760                         };
+<a name="l00761"></a>00761 
+<a name="l00782"></a>00782                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1migamma__ref.html#6ab1cf56dd7b718c285ee65d8953cf3e">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
+<a name="l00783"></a>00783 
+<a name="l00784"></a>00784                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
+<a name="l00785"></a>00785         };
+<a name="l00786"></a>00786 
+<a name="l00787"></a>00787 
+<a name="l00788"></a>00788         UIREGISTER(migamma_ref);
+<a name="l00789"></a>00789 
+<a name="l00799"></a><a class="code" href="classbdm_1_1elognorm.html">00799</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1elognorm.html">elognorm</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm</a>&lt;ldmat&gt;
+<a name="l00800"></a>00800         {
+<a name="l00801"></a>00801                 <span class="keyword">public</span>:
+<a name="l00802"></a><a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba">00802</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#8b948e2bce1253765a2542199913aaba" title="Returns a sample,  from density .">sample</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;ldmat&gt;::sample</a>() );};
+<a name="l00803"></a><a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea">00803</a>                         vec <a class="code" href="classbdm_1_1elognorm.html#adb41e4f4d6600dec6f8c1dbc5ed9eea" title="return expected value">mean</a>()<span class="keyword"> const </span>{vec var=<a class="code" href="classbdm_1_1enorm.html#729c75ef0fa8abae03d58ad1f81e6773" title="return expected variance (not covariance!)">enorm&lt;ldmat&gt;::variance</a>();<span class="keywordflow">return</span> exp ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> - 0.5*var );};
 <a name="l00804"></a>00804 
-<a name="l00805"></a>00805         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
-<a name="l00806"></a>00806         {
-<a name="l00807"></a>00807                 <span class="keyword">protected</span>:
-<a name="l00808"></a>00808                         eWishartCh W;
-<a name="l00809"></a>00809                         <span class="keywordtype">int</span> p;
-<a name="l00810"></a>00810                         <span class="keywordtype">double</span> delta;
-<a name="l00811"></a>00811                 <span class="keyword">public</span>:
-<a name="l00812"></a>00812                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
-<a name="l00813"></a>00813                                 delta = delta0;
-<a name="l00814"></a>00814                                 W.set_parameters ( inv ( Y0 ),delta0 ); 
-<a name="l00815"></a>00815                                 dim = W.dimension(); p=Y0.rows();
-<a name="l00816"></a>00816                         }
-<a name="l00817"></a>00817                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
-<a name="l00818"></a>00818                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
-<a name="l00819"></a>00819                         {
-<a name="l00820"></a>00820                                 mat Ch ( p,p );
-<a name="l00821"></a>00821                                 mat iCh ( p,p );
-<a name="l00822"></a>00822                                 copy_vector ( dim, y0._data(), Ch._data() );
-<a name="l00823"></a>00823                                 
-<a name="l00824"></a>00824                                 iCh=inv ( Ch );
-<a name="l00825"></a>00825                                 W.setY ( iCh );
-<a name="l00826"></a>00826                         }
-<a name="l00827"></a>00827                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
-<a name="l00828"></a>00828                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
-<a name="l00829"></a>00829                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
-<a name="l00830"></a>00830                                  
-<a name="l00831"></a>00831                                 copy_vector(p*p,val._data(),X._Ch()._data());
-<a name="l00832"></a>00832                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
-<a name="l00833"></a>00833                                 <span class="comment">// compute  </span>
-<a name="l00834"></a>00834 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
-<a name="l00835"></a>00835                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
-<a name="l00836"></a>00836                                 
-<a name="l00837"></a>00837                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); 
-<a name="l00838"></a>00838                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
-<a name="l00839"></a>00839                                 
-<a name="l00840"></a>00840 <span class="comment">/*                              if (0) {</span>
-<a name="l00841"></a>00841 <span class="comment">                                        mat XX=X.to_mat();</span>
-<a name="l00842"></a>00842 <span class="comment">                                        mat YY=Y.to_mat();</span>
-<a name="l00843"></a>00843 <span class="comment">                                        </span>
-<a name="l00844"></a>00844 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
-<a name="l00845"></a>00845 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
-<a name="l00846"></a>00846 <span class="comment">                                }*/</span>
-<a name="l00847"></a>00847                                 <span class="keywordflow">return</span> log1;                            
-<a name="l00848"></a>00848                         };
-<a name="l00849"></a>00849                         
-<a name="l00850"></a>00850         };
-<a name="l00851"></a>00851 
-<a name="l00852"></a>00852         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
-<a name="l00853"></a>00853         {
-<a name="l00854"></a>00854                 <span class="keyword">protected</span>:
-<a name="l00855"></a>00855                         eiWishartCh eiW;
-<a name="l00857"></a>00857                         <span class="keywordtype">double</span> sqd;
-<a name="l00858"></a>00858                         <span class="comment">//reference point for diagonal</span>
-<a name="l00859"></a>00859                         vec refl;
-<a name="l00860"></a>00860                         <span class="keywordtype">double</span> l;
-<a name="l00861"></a>00861                         <span class="keywordtype">int</span> p;
-<a name="l00862"></a>00862                 <span class="keyword">public</span>:
-<a name="l00863"></a>00863                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
-<a name="l00864"></a>00864                         {
-<a name="l00865"></a>00865                                 p=p0;
-<a name="l00866"></a>00866                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
-<a name="l00867"></a>00867                                 sqd=sqrt ( delta-p-1 );
-<a name="l00868"></a>00868                                 l=l0;
-<a name="l00869"></a>00869                                 refl=pow(ref0,1-l);
-<a name="l00870"></a>00870                                 
-<a name="l00871"></a>00871                                 eiW.set_parameters ( eye ( p ),delta );
-<a name="l00872"></a>00872                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
-<a name="l00873"></a>00873                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
-<a name="l00874"></a>00874                         }
-<a name="l00875"></a>00875                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
-<a name="l00876"></a>00876                                 vec z=c;
-<a name="l00877"></a>00877                                 <span class="keywordtype">int</span> ri=0;
-<a name="l00878"></a>00878                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
-<a name="l00879"></a>00879                                         z(i) = pow(z(i),l)*refl(ri);
-<a name="l00880"></a>00880                                         ri++;
-<a name="l00881"></a>00881                                 }
-<a name="l00882"></a>00882 
-<a name="l00883"></a>00883                                 eiW._setY ( sqd*z );
-<a name="l00884"></a>00884                         }
-<a name="l00885"></a>00885         };
+<a name="l00805"></a>00805         };
+<a name="l00806"></a>00806 
+<a name="l00818"></a><a class="code" href="classbdm_1_1mlognorm.html">00818</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1mlognorm.html" title="Log-Normal random walk.">mlognorm</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>
+<a name="l00819"></a>00819         {
+<a name="l00820"></a>00820                 <span class="keyword">protected</span>:
+<a name="l00821"></a>00821                         <a class="code" href="classbdm_1_1elognorm.html">elognorm</a> eno;
+<a name="l00823"></a><a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a">00823</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;
+<a name="l00825"></a><a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2">00825</a>                         vec &amp;<a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>;
+<a name="l00826"></a>00826                 <span class="keyword">public</span>:
+<a name="l00828"></a><a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41">00828</a>                         <a class="code" href="classbdm_1_1mlognorm.html#a5d6eb2688d02e0348b96c4fbd7bde41" title="Constructor.">mlognorm</a> ( ) : eno (), <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a> ( eno._mu() ) {<a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eno;};
+<a name="l00830"></a><a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f">00830</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#604cab0e8a76f9041dc3c606043bb39f" title="Set value of k.">set_parameters</a> ( <span class="keywordtype">int</span> size, <span class="keywordtype">double</span> k )
+<a name="l00831"></a>00831                         {
+<a name="l00832"></a>00832                                 <a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a> = 0.5*log ( k*k+1 );
+<a name="l00833"></a>00833                                 eno.<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( zeros ( size ),2*<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>*eye ( size ) );
+<a name="l00834"></a>00834 
+<a name="l00835"></a>00835                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a> = size;
+<a name="l00836"></a>00836                         };
+<a name="l00837"></a>00837 
+<a name="l00838"></a><a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e">00838</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#9106d8fd8bdf2b6be675ffd8f3ca584e" title="Update ep so that it represents this mpdf conditioned on rvc = cond.">condition</a> ( <span class="keyword">const</span> vec &amp;val )
+<a name="l00839"></a>00839                         {
+<a name="l00840"></a>00840                                 <a class="code" href="classbdm_1_1mlognorm.html#7d0063f77d899ef22e8c5edd642176d2" title="access">mu</a>=log ( val )-<a class="code" href="classbdm_1_1mlognorm.html#a51128a2e503b8b2ce698244b9e0db1a" title="parameter 1/2*sigma^2">sig2</a>;<span class="comment">//elem_mult ( refl,pow ( val,l ) );</span>
+<a name="l00841"></a>00841                         };
+<a name="l00842"></a>00842 
+<a name="l00861"></a>00861                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlognorm.html#3a130457942be64ee9544e8dff00d09b">from_setting</a>( <span class="keyword">const</span> Setting &amp;root );
+<a name="l00862"></a>00862 
+<a name="l00863"></a>00863                         <span class="comment">// TODO dodelat void to_setting( Setting &amp;root ) const;</span>
+<a name="l00864"></a>00864 
+<a name="l00865"></a>00865         };
+<a name="l00866"></a>00866         
+<a name="l00867"></a>00867         UIREGISTER(mlognorm);
+<a name="l00868"></a>00868 
+<a name="l00872"></a><a class="code" href="classbdm_1_1eWishartCh.html">00872</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eWishartCh.html">eWishartCh</a> : <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l00873"></a>00873         {
+<a name="l00874"></a>00874                 <span class="keyword">protected</span>:
+<a name="l00876"></a><a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490">00876</a>                         <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;
+<a name="l00878"></a><a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f">00878</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;
+<a name="l00880"></a><a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3">00880</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>;
+<a name="l00881"></a>00881                 <span class="keyword">public</span>:
+<a name="l00882"></a>00882                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0 ) {<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>=<a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> ( Y0 );<a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>=delta0; <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>=<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classsqmat.html#071e80ced9cc3b8cbb360fa7462eb646" title="Reimplementing common functions of mat: cols().">rows</a>(); <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>*<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>; }
+<a name="l00883"></a>00883                         mat sample_mat()<span class="keyword"> const</span>
+<a name="l00884"></a>00884 <span class="keyword">                        </span>{
+<a name="l00885"></a>00885                                 mat X=zeros ( <a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>,<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a> );
 <a name="l00886"></a>00886 
-<a name="l00888"></a>00888         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
-<a name="l00894"></a><a class="code" href="classbdm_1_1eEmp.html">00894</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
-<a name="l00895"></a>00895         {
-<a name="l00896"></a>00896                 <span class="keyword">protected</span> :
-<a name="l00898"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">00898</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
-<a name="l00900"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">00900</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
-<a name="l00902"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">00902</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
-<a name="l00903"></a>00903                 <span class="keyword">public</span>:
-<a name="l00906"></a>00906                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
-<a name="l00907"></a>00907                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
-<a name="l00909"></a>00909 
-<a name="l00911"></a>00911                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
-<a name="l00913"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">00913</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
-<a name="l00915"></a>00915                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
-<a name="l00917"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">00917</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
-<a name="l00919"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">00919</a>                         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l00921"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">00921</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
-<a name="l00923"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">00923</a>                         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l00925"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">00925</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
-<a name="l00927"></a>00927                         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC );
-<a name="l00929"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">00929</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
-<a name="l00931"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">00931</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
-<a name="l00932"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">00932</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
-<a name="l00933"></a>00933 <span class="keyword">                        </span>{
-<a name="l00934"></a>00934                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00935"></a>00935                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
-<a name="l00936"></a>00936                                 <span class="keywordflow">return</span> pom;
-<a name="l00937"></a>00937                         }
-<a name="l00938"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">00938</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
-<a name="l00939"></a>00939 <span class="keyword">                        </span>{
-<a name="l00940"></a>00940                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00941"></a>00941                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
-<a name="l00942"></a>00942                                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
-<a name="l00943"></a>00943                         }
-<a name="l00945"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">00945</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
-<a name="l00946"></a>00946 <span class="keyword">                        </span>{
-<a name="l00947"></a>00947                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
-<a name="l00948"></a>00948                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00949"></a>00949                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00950"></a>00950                                 lb = std::numeric_limits&lt;double&gt;::infinity();
-<a name="l00951"></a>00951                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
-<a name="l00952"></a>00952                                 <span class="keywordtype">int</span> j;
-<a name="l00953"></a>00953                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
-<a name="l00954"></a>00954                                 {
-<a name="l00955"></a>00955                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
-<a name="l00956"></a>00956                                         {
-<a name="l00957"></a>00957                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l00958"></a>00958                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
-<a name="l00959"></a>00959                                         }
-<a name="l00960"></a>00960                                 }
-<a name="l00961"></a>00961                         }
+<a name="l00887"></a>00887                                 <span class="comment">//sample diagonal</span>
+<a name="l00888"></a>00888                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eWishartCh.html#b745c73faef785009484180582050a1f" title="dimension of matrix ">p</a>;i++ )
+<a name="l00889"></a>00889                                 {
+<a name="l00890"></a>00890                                         GamRNG.<a class="code" href="classitpp_1_1Gamma__RNG.html#dfaae19411e39aa87e1f72e409b6babe" title="Set lambda.">setup</a> ( 0.5* ( <a class="code" href="classbdm_1_1eWishartCh.html#1879a14d7d2bb05062523b189baa11c3" title="degrees of freedom ">delta</a>-i ) , 0.5 ); <span class="comment">// no +1 !! index if from 0</span>
+<a name="l00891"></a>00891 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00892"></a>00892 <span class="preprocessor"></span>                                        X ( i,i ) =sqrt ( GamRNG() );
+<a name="l00893"></a>00893                                 }
+<a name="l00894"></a>00894                                 <span class="comment">//do the rest</span>
+<a name="l00895"></a>00895                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;p;i++ )
+<a name="l00896"></a>00896                                 {
+<a name="l00897"></a>00897                                         <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> j=i+1;j&lt;p;j++ )
+<a name="l00898"></a>00898                                         {
+<a name="l00899"></a>00899 <span class="preprocessor">#pragma omp critical</span>
+<a name="l00900"></a>00900 <span class="preprocessor"></span>                                                X ( i,j ) =NorRNG.sample();
+<a name="l00901"></a>00901                                         }
+<a name="l00902"></a>00902                                 }
+<a name="l00903"></a>00903                                 <span class="keywordflow">return</span> X*<a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>();<span class="comment">// return upper triangular part of the decomposition</span>
+<a name="l00904"></a>00904                         }
+<a name="l00905"></a><a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a">00905</a>                         vec <a class="code" href="classbdm_1_1eWishartCh.html#8f2154b8b5be8f4c5788f261b6d57b9a" title="Returns a sample,  from density .">sample</a> ()<span class="keyword"> const</span>
+<a name="l00906"></a>00906 <span class="keyword">                        </span>{
+<a name="l00907"></a>00907                                 <span class="keywordflow">return</span> vec ( sample_mat()._data(),p*p );
+<a name="l00908"></a>00908                         }
+<a name="l00910"></a><a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981">00910</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#4eee757c0535c2a88bb20f0767c64981" title="fast access function y0 will be copied into Y.Ch.">setY</a> ( <span class="keyword">const</span> mat &amp;Ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,Ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() );}
+<a name="l00912"></a><a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a">00912</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eWishartCh.html#7eac414ec10b85aa5536b0092c57bc4a" title="fast access function y0 will be copied into Y.Ch.">_setY</a> ( <span class="keyword">const</span> vec &amp;ch0 ) {copy_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>, ch0._data(), <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>.<a class="code" href="classchmat.html#9c50d31c999d85d8e9d8cf2b69b6ac8c" title="Access function.">_Ch</a>()._data() ); }
+<a name="l00914"></a><a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e">00914</a>                         <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; <a class="code" href="classbdm_1_1eWishartCh.html#1708cacb5d8cb1b96395d35f5327cb7e" title="access function">getY</a>()<span class="keyword">const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eWishartCh.html#1b42f9284a32f23b0b253a628cda7490" title="Upper-Triagle of Choleski decomposition of .">Y</a>;}
+<a name="l00915"></a>00915         };
+<a name="l00916"></a>00916 
+<a name="l00917"></a>00917         <span class="keyword">class </span>eiWishartCh: <span class="keyword">public</span> epdf
+<a name="l00918"></a>00918         {
+<a name="l00919"></a>00919                 <span class="keyword">protected</span>:
+<a name="l00920"></a>00920                         eWishartCh W;
+<a name="l00921"></a>00921                         <span class="keywordtype">int</span> p;
+<a name="l00922"></a>00922                         <span class="keywordtype">double</span> delta;
+<a name="l00923"></a>00923                 <span class="keyword">public</span>:
+<a name="l00924"></a>00924                         <span class="keywordtype">void</span> set_parameters ( <span class="keyword">const</span> mat &amp;Y0, <span class="keyword">const</span> <span class="keywordtype">double</span> delta0) {
+<a name="l00925"></a>00925                                 delta = delta0;
+<a name="l00926"></a>00926                                 W.set_parameters ( inv ( Y0 ),delta0 ); 
+<a name="l00927"></a>00927                                 dim = W.dimension(); p=Y0.rows();
+<a name="l00928"></a>00928                         }
+<a name="l00929"></a>00929                         vec sample()<span class="keyword"> const </span>{mat iCh; iCh=inv ( W.sample_mat() ); <span class="keywordflow">return</span> vec ( iCh._data(),<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );}
+<a name="l00930"></a>00930                         <span class="keywordtype">void</span> _setY ( <span class="keyword">const</span> vec &amp;y0 )
+<a name="l00931"></a>00931                         {
+<a name="l00932"></a>00932                                 mat Ch ( p,p );
+<a name="l00933"></a>00933                                 mat iCh ( p,p );
+<a name="l00934"></a>00934                                 copy_vector ( dim, y0._data(), Ch._data() );
+<a name="l00935"></a>00935                                 
+<a name="l00936"></a>00936                                 iCh=inv ( Ch );
+<a name="l00937"></a>00937                                 W.setY ( iCh );
+<a name="l00938"></a>00938                         }
+<a name="l00939"></a>00939                         <span class="keyword">virtual</span> <span class="keywordtype">double</span> evallog ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{
+<a name="l00940"></a>00940                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> X(p);
+<a name="l00941"></a>00941                                 <span class="keyword">const</span> <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a>&amp; Y=W.getY();
+<a name="l00942"></a>00942                                  
+<a name="l00943"></a>00943                                 copy_vector(p*p,val._data(),X._Ch()._data());
+<a name="l00944"></a>00944                                 <a class="code" href="classchmat.html" title="Symmetric matrix stored in square root decomposition using upper cholesky.">chmat</a> iX(p);X.inv(iX);
+<a name="l00945"></a>00945                                 <span class="comment">// compute  </span>
+<a name="l00946"></a>00946 <span class="comment">//                              \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)},</span>
+<a name="l00947"></a>00947                                 mat M=Y.<a class="code" href="classchmat.html#045addd685f8d978efda232d7dcb070e" title="Conversion to full matrix.">to_mat</a>()*iX.to_mat();
+<a name="l00948"></a>00948                                 
+<a name="l00949"></a>00949                                 <span class="keywordtype">double</span> log1 = 0.5*p*(2*Y.<a class="code" href="classchmat.html#b504ca818203b13e667cb3c503980382" title="Logarithm of a determinant.">logdet</a>())-0.5*(delta+p+1)*(2*X.logdet())-0.5*trace(M); 
+<a name="l00950"></a>00950                                 <span class="comment">//Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!!</span>
+<a name="l00951"></a>00951                                 
+<a name="l00952"></a>00952 <span class="comment">/*                              if (0) {</span>
+<a name="l00953"></a>00953 <span class="comment">                                        mat XX=X.to_mat();</span>
+<a name="l00954"></a>00954 <span class="comment">                                        mat YY=Y.to_mat();</span>
+<a name="l00955"></a>00955 <span class="comment">                                        </span>
+<a name="l00956"></a>00956 <span class="comment">                                        double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); </span>
+<a name="l00957"></a>00957 <span class="comment">                                        cout &lt;&lt; log1 &lt;&lt; "," &lt;&lt; log2 &lt;&lt; endl;</span>
+<a name="l00958"></a>00958 <span class="comment">                                }*/</span>
+<a name="l00959"></a>00959                                 <span class="keywordflow">return</span> log1;                            
+<a name="l00960"></a>00960                         };
+<a name="l00961"></a>00961                         
 <a name="l00962"></a>00962         };
 <a name="l00963"></a>00963 
-<a name="l00964"></a>00964 
-<a name="l00966"></a>00966 
-<a name="l00967"></a>00967         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00968"></a>00968         <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
-<a name="l00969"></a>00969         {
-<a name="l00970"></a>00970 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
-<a name="l00971"></a>00971                 mu = mu0;
-<a name="l00972"></a>00972                 R = R0;
-<a name="l00973"></a>00973                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
-<a name="l00974"></a>00974         };
-<a name="l00975"></a>00975 
-<a name="l00976"></a>00976         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00977"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">00977</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
-<a name="l00978"></a>00978         {
-<a name="l00979"></a>00979                 <span class="comment">//</span>
-<a name="l00980"></a>00980         };
-<a name="l00981"></a>00981 
-<a name="l00982"></a>00982 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l00983"></a>00983 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
-<a name="l00984"></a>00984 <span class="comment">//      //</span>
-<a name="l00985"></a>00985 <span class="comment">// };</span>
-<a name="l00986"></a>00986 
-<a name="l00987"></a>00987         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l00988"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">00988</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
-<a name="l00989"></a>00989 <span class="keyword">        </span>{
-<a name="l00990"></a>00990                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l00991"></a>00991 <span class="preprocessor">#pragma omp critical</span>
-<a name="l00992"></a>00992 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l00993"></a>00993                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l00964"></a>00964         <span class="keyword">class </span>rwiWishartCh : <span class="keyword">public</span> mpdf
+<a name="l00965"></a>00965         {
+<a name="l00966"></a>00966                 <span class="keyword">protected</span>:
+<a name="l00967"></a>00967                         eiWishartCh eiW;
+<a name="l00969"></a>00969                         <span class="keywordtype">double</span> sqd;
+<a name="l00970"></a>00970                         <span class="comment">//reference point for diagonal</span>
+<a name="l00971"></a>00971                         vec refl;
+<a name="l00972"></a>00972                         <span class="keywordtype">double</span> l;
+<a name="l00973"></a>00973                         <span class="keywordtype">int</span> p;
+<a name="l00974"></a>00974                 <span class="keyword">public</span>:
+<a name="l00975"></a>00975                         <span class="keywordtype">void</span> set_parameters ( <span class="keywordtype">int</span> p0, <span class="keywordtype">double</span> k, vec ref0, <span class="keywordtype">double</span> l0  )
+<a name="l00976"></a>00976                         {
+<a name="l00977"></a>00977                                 p=p0;
+<a name="l00978"></a>00978                                 <span class="keywordtype">double</span> delta = 2/(k*k)+p+3;
+<a name="l00979"></a>00979                                 sqd=sqrt ( delta-p-1 );
+<a name="l00980"></a>00980                                 l=l0;
+<a name="l00981"></a>00981                                 refl=pow(ref0,1-l);
+<a name="l00982"></a>00982                                 
+<a name="l00983"></a>00983                                 eiW.set_parameters ( eye ( p ),delta );
+<a name="l00984"></a>00984                                 <a class="code" href="classbdm_1_1mpdf.html#5eea43c56d38e4441bfb30270db949c0" title="pointer to internal epdf">ep</a>=&amp;eiW;
+<a name="l00985"></a>00985                                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=eiW.dimension();
+<a name="l00986"></a>00986                         }
+<a name="l00987"></a>00987                         <span class="keywordtype">void</span> condition ( <span class="keyword">const</span> vec &amp;c ) {
+<a name="l00988"></a>00988                                 vec z=c;
+<a name="l00989"></a>00989                                 <span class="keywordtype">int</span> ri=0;
+<a name="l00990"></a>00990                                 <span class="keywordflow">for</span>(<span class="keywordtype">int</span> i=0;i&lt;p*p;i+=(p+1)){<span class="comment">//trace diagonal element</span>
+<a name="l00991"></a>00991                                         z(i) = pow(z(i),l)*refl(ri);
+<a name="l00992"></a>00992                                         ri++;
+<a name="l00993"></a>00993                                 }
 <a name="l00994"></a>00994 
-<a name="l00995"></a>00995                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l00996"></a>00996                 <span class="keywordflow">return</span> smp;
+<a name="l00995"></a>00995                                 eiW._setY ( sqd*z );
+<a name="l00996"></a>00996                         }
 <a name="l00997"></a>00997         };
 <a name="l00998"></a>00998 
-<a name="l00999"></a>00999         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01000"></a>01000         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
-<a name="l01001"></a>01001 <span class="keyword">        </span>{
-<a name="l01002"></a>01002                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
-<a name="l01003"></a>01003                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
-<a name="l01004"></a>01004                 vec pom;
-<a name="l01005"></a>01005                 <span class="keywordtype">int</span> i;
-<a name="l01006"></a>01006 
-<a name="l01007"></a>01007                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
-<a name="l01008"></a>01008                 {
-<a name="l01009"></a>01009 <span class="preprocessor">#pragma omp critical</span>
-<a name="l01010"></a>01010 <span class="preprocessor"></span>                        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
-<a name="l01011"></a>01011                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
-<a name="l01012"></a>01012                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
-<a name="l01013"></a>01013                         X.set_col ( i, pom );
-<a name="l01014"></a>01014                 }
-<a name="l01015"></a>01015 
-<a name="l01016"></a>01016                 <span class="keywordflow">return</span> X;
-<a name="l01017"></a>01017         };
-<a name="l01018"></a>01018 
-<a name="l01019"></a>01019 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01020"></a>01020 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
-<a name="l01021"></a>01021 <span class="comment">//      double pdfl,e;</span>
-<a name="l01022"></a>01022 <span class="comment">//      pdfl = evallog ( val );</span>
-<a name="l01023"></a>01023 <span class="comment">//      e = exp ( pdfl );</span>
-<a name="l01024"></a>01024 <span class="comment">//      return e;</span>
-<a name="l01025"></a>01025 <span class="comment">// };</span>
-<a name="l01026"></a>01026 
-<a name="l01027"></a>01027         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01028"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01028</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
-<a name="l01029"></a>01029 <span class="keyword">        </span>{
-<a name="l01030"></a>01030                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l01031"></a>01031                 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
-<a name="l01032"></a>01032                 <span class="keywordflow">return</span>  tmp;
-<a name="l01033"></a>01033         };
-<a name="l01034"></a>01034 
-<a name="l01035"></a>01035         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01036"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01036</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
-<a name="l01037"></a>01037 <span class="keyword">        </span>{
-<a name="l01038"></a>01038                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
-<a name="l01039"></a>01039                 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
-<a name="l01040"></a>01040                 <span class="keywordflow">return</span> tmp;
-<a name="l01041"></a>01041         };
-<a name="l01042"></a>01042 
-<a name="l01043"></a>01043         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01044"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01044</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
-<a name="l01045"></a>01045         {
-<a name="l01046"></a>01046                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
-<a name="l01047"></a>01047                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
-<a name="l01048"></a>01048 
-<a name="l01049"></a>01049                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
-<a name="l01050"></a>01050                 A = A0;
-<a name="l01051"></a>01051                 mu_const = mu0;
-<a name="l01052"></a>01052                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
-<a name="l01053"></a>01053         }
-<a name="l01054"></a>01054 
-<a name="l01055"></a>01055 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01056"></a>01056 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
-<a name="l01057"></a>01057 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l01058"></a>01058 <span class="comment">//      vec smp = epdf.sample();</span>
-<a name="l01059"></a>01059 <span class="comment">//      lik = epdf.eval ( smp );</span>
-<a name="l01060"></a>01060 <span class="comment">//      return smp;</span>
-<a name="l01061"></a>01061 <span class="comment">// }</span>
-<a name="l01062"></a>01062 
-<a name="l01063"></a>01063 <span class="comment">// template&lt;class sq_T&gt;</span>
-<a name="l01064"></a>01064 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
-<a name="l01065"></a>01065 <span class="comment">//      int i;</span>
-<a name="l01066"></a>01066 <span class="comment">//      int dim = rv.count();</span>
-<a name="l01067"></a>01067 <span class="comment">//      mat Smp ( dim,n );</span>
-<a name="l01068"></a>01068 <span class="comment">//      vec smp ( dim );</span>
-<a name="l01069"></a>01069 <span class="comment">//      this-&gt;condition ( cond );</span>
-<a name="l01070"></a>01070 <span class="comment">//</span>
-<a name="l01071"></a>01071 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
-<a name="l01072"></a>01072 <span class="comment">//              smp = epdf.sample();</span>
-<a name="l01073"></a>01073 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
-<a name="l01074"></a>01074 <span class="comment">//              Smp.set_col ( i ,smp );</span>
-<a name="l01075"></a>01075 <span class="comment">//      }</span>
-<a name="l01076"></a>01076 <span class="comment">//</span>
-<a name="l01077"></a>01077 <span class="comment">//      return Smp;</span>
-<a name="l01078"></a>01078 <span class="comment">// }</span>
-<a name="l01079"></a>01079 
-<a name="l01080"></a>01080         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01081"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01081</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
-<a name="l01082"></a>01082         {
-<a name="l01083"></a>01083                 _mu = A*cond + mu_const;
-<a name="l01084"></a>01084 <span class="comment">//R is already assigned;</span>
-<a name="l01085"></a>01085         }
-<a name="l01086"></a>01086 
-<a name="l01087"></a>01087         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01088"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01088</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
-<a name="l01089"></a>01089 <span class="keyword">        </span>{
-<a name="l01090"></a>01090                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
-<a name="l01091"></a>01091                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01092"></a>01092 
-<a name="l01093"></a>01093                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
-<a name="l01094"></a>01094 
-<a name="l01095"></a>01095                 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>;
-<a name="l01096"></a>01096                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
-<a name="l01097"></a>01097                 tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
-<a name="l01098"></a>01098                 <span class="keywordflow">return</span> tmp;
-<a name="l01099"></a>01099         }
-<a name="l01100"></a>01100 
-<a name="l01101"></a>01101         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01102"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01102</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
-<a name="l01103"></a>01103 <span class="keyword">        </span>{
-<a name="l01104"></a>01104 
-<a name="l01105"></a>01105                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
+<a name="l01000"></a>01000         <span class="keyword">enum</span> RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 };
+<a name="l01006"></a><a class="code" href="classbdm_1_1eEmp.html">01006</a>         <span class="keyword">class </span><a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a>: <span class="keyword">public</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>
+<a name="l01007"></a>01007         {
+<a name="l01008"></a>01008                 <span class="keyword">protected</span> :
+<a name="l01010"></a><a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031">01010</a>                         <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;
+<a name="l01012"></a><a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d">01012</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;
+<a name="l01014"></a><a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3">01014</a>                         Array&lt;vec&gt; <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;
+<a name="l01015"></a>01015                 <span class="keyword">public</span>:
+<a name="l01018"></a>01018                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( ) :<a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( ),<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( ) {};
+<a name="l01019"></a>01019                         <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1eEmp.html" title="Weighted empirical density.">eEmp</a> &amp;e ) : <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> ( e ), <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ), <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( e.<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ) {};
+<a name="l01021"></a>01021 
+<a name="l01023"></a>01023                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#7cfd383180b486fe4526bdf0179350c0" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> vec &amp;w0, <span class="keyword">const</span> epdf* pdf0 );
+<a name="l01025"></a><a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7">01025</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 , <span class="keywordtype">int</span> <a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a> ) {<a class="code" href="classbdm_1_1eEmp.html#cef74aa5f87d10d440b9b1e8bc78c1e7" title="Set samples and weights.">set_statistics</a> ( ones ( n ) /n,pdf0 );};
+<a name="l01027"></a>01027                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b62d802b8ef39f7c4dcbeb366c90951a" title="Set sample.">set_samples</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>* pdf0 );
+<a name="l01029"></a><a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1">01029</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#c74c281d652356c19b6b079e42ca7ef1" title="Set sample.">set_parameters</a> ( <span class="keywordtype">int</span> n0, <span class="keywordtype">bool</span> copy=<span class="keyword">true</span> ) {<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>=n0; <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>.set_size ( n0,copy );<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>.set_size ( n0,copy );};
+<a name="l01031"></a><a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef">01031</a>                         vec&amp; <a class="code" href="classbdm_1_1eEmp.html#d7f83cc0415cd44ae7cc8b4bdad93aef" title="Potentially dangerous, use with care.">_w</a>()  {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l01033"></a><a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714">01033</a>                         <span class="keyword">const</span> vec&amp; <a class="code" href="classbdm_1_1eEmp.html#b7d7106f486e3fad38590914a693d714" title="Potentially dangerous, use with care.">_w</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a>;};
+<a name="l01035"></a><a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2">01035</a>                         Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#c24966b0aaeb767bc8a6b4fd60931be2" title="access function">_samples</a>() {<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l01037"></a><a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2">01037</a>                         <span class="keyword">const</span> Array&lt;vec&gt;&amp; <a class="code" href="classbdm_1_1eEmp.html#b59af0efdb009d98ea8ebfa965e74ae2" title="access function">_samples</a>()<span class="keyword"> const </span>{<span class="keywordflow">return</span> <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a>;};
+<a name="l01039"></a>01039                         ivec <a class="code" href="classbdm_1_1eEmp.html#f06ce255de5dbb2313f52ee51f82ba3d" title="Function performs resampling, i.e. removal of low-weight samples and duplication...">resample</a> ( RESAMPLING_METHOD method=SYSTEMATIC );
+<a name="l01041"></a><a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270">01041</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#97f1e07b5ae6eebc91c7365f0f88d270" title="inherited operation : NOT implemneted">sample</a>()<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0;}
+<a name="l01043"></a><a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09">01043</a>                         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1eEmp.html#01654c014d3aa068f8d4ecba4be86d09" title="inherited operation : NOT implemneted">evallog</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const </span>{it_error ( <span class="stringliteral">"Not implemented"</span> );<span class="keywordflow">return</span> 0.0;}
+<a name="l01044"></a><a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9">01044</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>()<span class="keyword"> const</span>
+<a name="l01045"></a>01045 <span class="keyword">                        </span>{
+<a name="l01046"></a>01046                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01047"></a>01047                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l01048"></a>01048                                 <span class="keywordflow">return</span> pom;
+<a name="l01049"></a>01049                         }
+<a name="l01050"></a><a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87">01050</a>                         vec <a class="code" href="classbdm_1_1eEmp.html#05e9ebf467ede737cb6a3621d7fd3c87" title="return expected variance (not covariance!)">variance</a>()<span class="keyword"> const</span>
+<a name="l01051"></a>01051 <span class="keyword">                        </span>{
+<a name="l01052"></a>01052                                 vec pom=zeros ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01053"></a>01053                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ ) {pom+=pow ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ),2 ) *<a class="code" href="classbdm_1_1eEmp.html#9d39241aab7c4bbeb07c6d516421c67d" title="Sample weights .">w</a> ( i );}
+<a name="l01054"></a>01054                                 <span class="keywordflow">return</span> pom-pow ( <a class="code" href="classbdm_1_1eEmp.html#bbfcb4f868c7381298c281a256d8c4b9" title="return expected value">mean</a>(),2 );
+<a name="l01055"></a>01055                         }
+<a name="l01057"></a><a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f">01057</a>                         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1eEmp.html#b1c9df656144edf79ba2d885613f661f" title="For this class, qbounds are minimum and maximum value of the population!">qbounds</a> ( vec &amp;lb, vec &amp;ub, <span class="keywordtype">double</span> perc=0.95 )<span class="keyword"> const</span>
+<a name="l01058"></a>01058 <span class="keyword">                        </span>{
+<a name="l01059"></a>01059                                 <span class="comment">// lb in inf so than it will be pushed below;</span>
+<a name="l01060"></a>01060                                 lb.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01061"></a>01061                                 ub.set_size ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01062"></a>01062                                 lb = std::numeric_limits&lt;double&gt;::infinity();
+<a name="l01063"></a>01063                                 ub = -std::numeric_limits&lt;double&gt;::infinity();
+<a name="l01064"></a>01064                                 <span class="keywordtype">int</span> j;
+<a name="l01065"></a>01065                                 <span class="keywordflow">for</span> ( <span class="keywordtype">int</span> i=0;i&lt;<a class="code" href="classbdm_1_1eEmp.html#9798006271ca77629855113f1283a031" title="Number of particles.">n</a>;i++ )
+<a name="l01066"></a>01066                                 {
+<a name="l01067"></a>01067                                         <span class="keywordflow">for</span> ( j=0;j&lt;<a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>; j++ )
+<a name="l01068"></a>01068                                         {
+<a name="l01069"></a>01069                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &lt;lb ( j ) ) {lb ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l01070"></a>01070                                                 <span class="keywordflow">if</span> ( <a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j ) &gt;ub ( j ) ) {ub ( j ) =<a class="code" href="classbdm_1_1eEmp.html#73d819553a0f268b055a087d2d4486f3" title="Samples .">samples</a> ( i ) ( j );}
+<a name="l01071"></a>01071                                         }
+<a name="l01072"></a>01072                                 }
+<a name="l01073"></a>01073                         }
+<a name="l01074"></a>01074         };
+<a name="l01075"></a>01075 
+<a name="l01076"></a>01076 
+<a name="l01078"></a>01078 
+<a name="l01079"></a>01079         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01080"></a>01080         <span class="keywordtype">void</span> enorm&lt;sq_T&gt;::set_parameters ( <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
+<a name="l01081"></a>01081         {
+<a name="l01082"></a>01082 <span class="comment">//Fixme test dimensions of mu0 and R0;</span>
+<a name="l01083"></a>01083                 mu = mu0;
+<a name="l01084"></a>01084                 R = R0;
+<a name="l01085"></a>01085                 <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> = mu0.length();
+<a name="l01086"></a>01086         };
+<a name="l01087"></a>01087 
+<a name="l01088"></a>01088         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01089"></a><a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2">01089</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1enorm.html#d2e0d3a1e30ab3ab04df2d0c43ae74a2" title="dupdate in exponential form (not really handy)">enorm&lt;sq_T&gt;::dupdate</a> ( mat &amp;v, <span class="keywordtype">double</span> nu )
+<a name="l01090"></a>01090         {
+<a name="l01091"></a>01091                 <span class="comment">//</span>
+<a name="l01092"></a>01092         };
+<a name="l01093"></a>01093 
+<a name="l01094"></a>01094 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01095"></a>01095 <span class="comment">// void enorm&lt;sq_T&gt;::tupdate ( double phi, mat &amp;vbar, double nubar ) {</span>
+<a name="l01096"></a>01096 <span class="comment">//      //</span>
+<a name="l01097"></a>01097 <span class="comment">// };</span>
+<a name="l01098"></a>01098 
+<a name="l01099"></a>01099         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01100"></a><a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766">01100</a>         vec <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a>()<span class="keyword"> const</span>
+<a name="l01101"></a>01101 <span class="keyword">        </span>{
+<a name="l01102"></a>01102                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01103"></a>01103 <span class="preprocessor">#pragma omp critical</span>
+<a name="l01104"></a>01104 <span class="preprocessor"></span>                NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l01105"></a>01105                 vec smp = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
 <a name="l01106"></a>01106 
-<a name="l01107"></a>01107                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
-<a name="l01108"></a>01108                 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
-<a name="l01109"></a>01109                 <span class="comment">//Permutation vector of the new R</span>
-<a name="l01110"></a>01110                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01111"></a>01111                 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
-<a name="l01112"></a>01112                 ivec perm=concat ( irvn , irvc );
-<a name="l01113"></a>01113                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
-<a name="l01114"></a>01114 
-<a name="l01115"></a>01115                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
-<a name="l01116"></a>01116                 mat S=Rn.to_mat();
-<a name="l01117"></a>01117                 <span class="comment">//fixme</span>
-<a name="l01118"></a>01118                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
-<a name="l01119"></a>01119                 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
-<a name="l01120"></a>01120                 mat S11 = S.get ( 0,n, 0, n );
-<a name="l01121"></a>01121                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l01122"></a>01122                 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
-<a name="l01123"></a>01123 
-<a name="l01124"></a>01124                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
-<a name="l01125"></a>01125                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
-<a name="l01126"></a>01126                 mat A=S12*inv ( S22 );
-<a name="l01127"></a>01127                 sq_T R_n ( S11 - A *S12.T() );
-<a name="l01128"></a>01128 
-<a name="l01129"></a>01129                 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
-<a name="l01130"></a>01130                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
-<a name="l01131"></a>01131                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
-<a name="l01132"></a>01132                 <span class="keywordflow">return</span> tmp;
-<a name="l01133"></a>01133         }
-<a name="l01134"></a>01134 
-<a name="l01136"></a>01136 
-<a name="l01137"></a>01137         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
-<a name="l01138"></a>01138         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
-<a name="l01139"></a>01139         {
-<a name="l01140"></a>01140                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
-<a name="l01141"></a>01141                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
-<a name="l01142"></a>01142                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
-<a name="l01143"></a>01143                 <span class="keywordflow">return</span> os;
-<a name="l01144"></a>01144         };
-<a name="l01145"></a>01145 
-<a name="l01146"></a>01146 }
-<a name="l01147"></a>01147 <span class="preprocessor">#endif //EF_H</span>
+<a name="l01107"></a>01107                 smp += <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01108"></a>01108                 <span class="keywordflow">return</span> smp;
+<a name="l01109"></a>01109         };
+<a name="l01110"></a>01110 
+<a name="l01111"></a>01111         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01112"></a>01112         mat <a class="code" href="classbdm_1_1enorm.html#e1a48f52351ec3a349bd443b713b1766" title="Returns a sample,  from density .">enorm&lt;sq_T&gt;::sample</a> ( <span class="keywordtype">int</span> N )<span class="keyword"> const</span>
+<a name="l01113"></a>01113 <span class="keyword">        </span>{
+<a name="l01114"></a>01114                 mat X ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,N );
+<a name="l01115"></a>01115                 vec x ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a> );
+<a name="l01116"></a>01116                 vec pom;
+<a name="l01117"></a>01117                 <span class="keywordtype">int</span> i;
+<a name="l01118"></a>01118 
+<a name="l01119"></a>01119                 <span class="keywordflow">for</span> ( i=0;i&lt;N;i++ )
+<a name="l01120"></a>01120                 {
+<a name="l01121"></a>01121 <span class="preprocessor">#pragma omp critical</span>
+<a name="l01122"></a>01122 <span class="preprocessor"></span>                        NorRNG.sample_vector ( <a class="code" href="classbdm_1_1epdf.html#16adac20ec7fe07e1ea0b27d917788ce" title="dimension of the random variable">dim</a>,x );
+<a name="l01123"></a>01123                         pom = <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.sqrt_mult ( x );
+<a name="l01124"></a>01124                         pom +=<a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>;
+<a name="l01125"></a>01125                         X.set_col ( i, pom );
+<a name="l01126"></a>01126                 }
+<a name="l01127"></a>01127 
+<a name="l01128"></a>01128                 <span class="keywordflow">return</span> X;
+<a name="l01129"></a>01129         };
+<a name="l01130"></a>01130 
+<a name="l01131"></a>01131 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01132"></a>01132 <span class="comment">// double enorm&lt;sq_T&gt;::eval ( const vec &amp;val ) const {</span>
+<a name="l01133"></a>01133 <span class="comment">//      double pdfl,e;</span>
+<a name="l01134"></a>01134 <span class="comment">//      pdfl = evallog ( val );</span>
+<a name="l01135"></a>01135 <span class="comment">//      e = exp ( pdfl );</span>
+<a name="l01136"></a>01136 <span class="comment">//      return e;</span>
+<a name="l01137"></a>01137 <span class="comment">// };</span>
+<a name="l01138"></a>01138 
+<a name="l01139"></a>01139         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01140"></a><a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3">01140</a>         <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#e13aeed5b543b2179bacdc4fa2ae47a3" title="Evaluate normalized log-probability.">enorm&lt;sq_T&gt;::evallog_nn</a> ( <span class="keyword">const</span> vec &amp;val )<span class="keyword"> const</span>
+<a name="l01141"></a>01141 <span class="keyword">        </span>{
+<a name="l01142"></a>01142                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01143"></a>01143                 <span class="keywordtype">double</span> tmp=-0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.invqform ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a>-val ) );<span class="comment">// - lognc();</span>
+<a name="l01144"></a>01144                 <span class="keywordflow">return</span>  tmp;
+<a name="l01145"></a>01145         };
+<a name="l01146"></a>01146 
+<a name="l01147"></a>01147         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01148"></a><a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32">01148</a>         <span class="keyword">inline</span> <span class="keywordtype">double</span> <a class="code" href="classbdm_1_1enorm.html#25785343aff102cc5df1cab08ba16d32" title="logarithm of the normalizing constant, ">enorm&lt;sq_T&gt;::lognc</a> ()<span class="keyword"> const</span>
+<a name="l01149"></a>01149 <span class="keyword">        </span>{
+<a name="l01150"></a>01150                 <span class="comment">// 1.83787706640935 = log(2pi)</span>
+<a name="l01151"></a>01151                 <span class="keywordtype">double</span> tmp=0.5* ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.cols() * 1.83787706640935 +<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.logdet() );
+<a name="l01152"></a>01152                 <span class="keywordflow">return</span> tmp;
+<a name="l01153"></a>01153         };
+<a name="l01154"></a>01154 
+<a name="l01155"></a>01155         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01156"></a><a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f">01156</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#5d18dec3167584338a4775c1d165d96f" title="Set A and R.">mlnorm&lt;sq_T&gt;::set_parameters</a> ( <span class="keyword">const</span> mat &amp;A0, <span class="keyword">const</span> vec &amp;mu0, <span class="keyword">const</span> sq_T &amp;R0 )
+<a name="l01157"></a>01157         {
+<a name="l01158"></a>01158                 it_assert_debug ( A0.rows() ==mu0.length(),<span class="stringliteral">""</span> );
+<a name="l01159"></a>01159                 it_assert_debug ( A0.rows() ==R0.rows(),<span class="stringliteral">""</span> );
+<a name="l01160"></a>01160 
+<a name="l01161"></a>01161                 <a class="code" href="classbdm_1_1epdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a>.set_parameters ( zeros ( A0.rows() ),R0 );
+<a name="l01162"></a>01162                 A = A0;
+<a name="l01163"></a>01163                 mu_const = mu0;
+<a name="l01164"></a>01164                 <a class="code" href="classbdm_1_1mpdf.html#7c1900976ff13dbc09c9729b3bbff9e6" title="dimension of the condition">dimc</a>=A0.cols();
+<a name="l01165"></a>01165         }
+<a name="l01166"></a>01166 
+<a name="l01167"></a>01167 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01168"></a>01168 <span class="comment">// vec mlnorm&lt;sq_T&gt;::samplecond (const  vec &amp;cond, double &amp;lik ) {</span>
+<a name="l01169"></a>01169 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01170"></a>01170 <span class="comment">//      vec smp = epdf.sample();</span>
+<a name="l01171"></a>01171 <span class="comment">//      lik = epdf.eval ( smp );</span>
+<a name="l01172"></a>01172 <span class="comment">//      return smp;</span>
+<a name="l01173"></a>01173 <span class="comment">// }</span>
+<a name="l01174"></a>01174 
+<a name="l01175"></a>01175 <span class="comment">// template&lt;class sq_T&gt;</span>
+<a name="l01176"></a>01176 <span class="comment">// mat mlnorm&lt;sq_T&gt;::samplecond (const vec &amp;cond, vec &amp;lik, int n ) {</span>
+<a name="l01177"></a>01177 <span class="comment">//      int i;</span>
+<a name="l01178"></a>01178 <span class="comment">//      int dim = rv.count();</span>
+<a name="l01179"></a>01179 <span class="comment">//      mat Smp ( dim,n );</span>
+<a name="l01180"></a>01180 <span class="comment">//      vec smp ( dim );</span>
+<a name="l01181"></a>01181 <span class="comment">//      this-&gt;condition ( cond );</span>
+<a name="l01182"></a>01182 <span class="comment">//</span>
+<a name="l01183"></a>01183 <span class="comment">//      for ( i=0; i&lt;n; i++ ) {</span>
+<a name="l01184"></a>01184 <span class="comment">//              smp = epdf.sample();</span>
+<a name="l01185"></a>01185 <span class="comment">//              lik ( i ) = epdf.eval ( smp );</span>
+<a name="l01186"></a>01186 <span class="comment">//              Smp.set_col ( i ,smp );</span>
+<a name="l01187"></a>01187 <span class="comment">//      }</span>
+<a name="l01188"></a>01188 <span class="comment">//</span>
+<a name="l01189"></a>01189 <span class="comment">//      return Smp;</span>
+<a name="l01190"></a>01190 <span class="comment">// }</span>
+<a name="l01191"></a>01191 
+<a name="l01192"></a>01192         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01193"></a><a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">01193</a>         <span class="keywordtype">void</span> <a class="code" href="classbdm_1_1mlnorm.html#0dafc0196e7e07fd06dc6716e0e18bc2">mlnorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> vec &amp;cond )
+<a name="l01194"></a>01194         {
+<a name="l01195"></a>01195                 _mu = A*cond + mu_const;
+<a name="l01196"></a>01196 <span class="comment">//R is already assigned;</span>
+<a name="l01197"></a>01197         }
+<a name="l01198"></a>01198 
+<a name="l01199"></a>01199         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01200"></a><a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80">01200</a>         <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* <a class="code" href="classbdm_1_1enorm.html#c2996bdaffad38fbe0fc776db54c9d80" title="Return marginal density on the given RV, the remainig rvs are intergrated out.">enorm&lt;sq_T&gt;::marginal</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01201"></a>01201 <span class="keyword">        </span>{
+<a name="l01202"></a>01202                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(), <span class="stringliteral">"rv description is not assigned"</span> );
+<a name="l01203"></a>01203                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01204"></a>01204 
+<a name="l01205"></a>01205                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,irvn ); <span class="comment">//select rows and columns of R</span>
+<a name="l01206"></a>01206 
+<a name="l01207"></a>01207                 <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>* tmp = <span class="keyword">new</span> <a class="code" href="classbdm_1_1enorm.html" title="Gaussian density with positive definite (decomposed) covariance matrix.">enorm&lt;sq_T&gt;</a>;
+<a name="l01208"></a>01208                 tmp-&gt;<a class="code" href="classbdm_1_1epdf.html#f423e28448dbb69ef4905295ec8de8ff" title="Name its rv.">set_rv</a> ( rvn );
+<a name="l01209"></a>01209                 tmp-&gt;<a class="code" href="classbdm_1_1enorm.html#b8322f2c11560871dd922c660f4771bb">set_parameters</a> ( <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn ), Rn );
+<a name="l01210"></a>01210                 <span class="keywordflow">return</span> tmp;
+<a name="l01211"></a>01211         }
+<a name="l01212"></a>01212 
+<a name="l01213"></a>01213         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01214"></a><a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26">01214</a>         <a class="code" href="classbdm_1_1mpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>* <a class="code" href="classbdm_1_1enorm.html#baea4d49c657342b58297d68cda16d26" title="Return conditional density on the given RV, the remaining rvs will be in conditioning...">enorm&lt;sq_T&gt;::condition</a> ( <span class="keyword">const</span> <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> &amp;rvn )<span class="keyword"> const</span>
+<a name="l01215"></a>01215 <span class="keyword">        </span>{
+<a name="l01216"></a>01216 
+<a name="l01217"></a>01217                 it_assert_debug ( <a class="code" href="classbdm_1_1epdf.html#c4b863ff84c7a4882fb3ad18556027f9" title="True if rv is assigned.">isnamed</a>(),<span class="stringliteral">"rvs are not assigned"</span> );
+<a name="l01218"></a>01218 
+<a name="l01219"></a>01219                 <a class="code" href="classbdm_1_1RV.html" title="Class representing variables, most often random variables.">RV</a> rvc = <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#aec44dabdf0a6d90fbae95e1356eda39" title="Subtract another variable from the current one.">subt</a> ( rvn );
+<a name="l01220"></a>01220                 it_assert_debug ( ( rvc.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() +rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ==<a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a>.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>() ),<span class="stringliteral">"wrong rvn"</span> );
+<a name="l01221"></a>01221                 <span class="comment">//Permutation vector of the new R</span>
+<a name="l01222"></a>01222                 ivec irvn = rvn.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01223"></a>01223                 ivec irvc = rvc.<a class="code" href="classbdm_1_1RV.html#cbebdb5e0d30101a6eb63550ef701c55">dataind</a> ( <a class="code" href="classbdm_1_1epdf.html#62c5b8ff71d9ebe6cd58d3c342eb1dc8" title="Description of the random variable.">rv</a> );
+<a name="l01224"></a>01224                 ivec perm=concat ( irvn , irvc );
+<a name="l01225"></a>01225                 sq_T Rn ( <a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>,perm );
+<a name="l01226"></a>01226 
+<a name="l01227"></a>01227                 <span class="comment">//fixme - could this be done in general for all sq_T?</span>
+<a name="l01228"></a>01228                 mat S=Rn.to_mat();
+<a name="l01229"></a>01229                 <span class="comment">//fixme</span>
+<a name="l01230"></a>01230                 <span class="keywordtype">int</span> n=rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>()-1;
+<a name="l01231"></a>01231                 <span class="keywordtype">int</span> end=<a class="code" href="classbdm_1_1enorm.html#2d92dde696b2a7a5b10ddef5d22ba2c2" title="Covariance matrix in decomposed form.">R</a>.rows()-1;
+<a name="l01232"></a>01232                 mat S11 = S.get ( 0,n, 0, n );
+<a name="l01233"></a>01233                 mat S12 = S.get ( 0, n , rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l01234"></a>01234                 mat S22 = S.get ( rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end, rvn.<a class="code" href="classbdm_1_1RV.html#de30156104f61d86c94f758861418089">_dsize</a>(), end );
+<a name="l01235"></a>01235 
+<a name="l01236"></a>01236                 vec mu1 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvn );
+<a name="l01237"></a>01237                 vec mu2 = <a class="code" href="classbdm_1_1enorm.html#c702a194720853570d08b65482f842c7" title="mean value">mu</a> ( irvc );
+<a name="l01238"></a>01238                 mat A=S12*inv ( S22 );
+<a name="l01239"></a>01239                 sq_T R_n ( S11 - A *S12.T() );
+<a name="l01240"></a>01240 
+<a name="l01241"></a>01241                 <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a>* tmp=<span class="keyword">new</span> <a class="code" href="classbdm_1_1mlnorm.html" title="Normal distributed linear function with linear function of mean value;.">mlnorm&lt;sq_T&gt;</a> ( );
+<a name="l01242"></a>01242                 tmp-&gt;set_rv ( rvn ); tmp-&gt;set_rvc ( rvc );
+<a name="l01243"></a>01243                 tmp-&gt;set_parameters ( A,mu1-A*mu2,R_n );
+<a name="l01244"></a>01244                 <span class="keywordflow">return</span> tmp;
+<a name="l01245"></a>01245         }
+<a name="l01246"></a>01246 
+<a name="l01248"></a>01248 
+<a name="l01249"></a>01249         <span class="keyword">template</span>&lt;<span class="keyword">class</span> sq_T&gt;
+<a name="l01250"></a>01250         std::ostream &amp;operator&lt;&lt; ( std::ostream &amp;os,  mlnorm&lt;sq_T&gt; &amp;ml )
+<a name="l01251"></a>01251         {
+<a name="l01252"></a>01252                 os &lt;&lt; <span class="stringliteral">"A:"</span>&lt;&lt; ml.A&lt;&lt;endl;
+<a name="l01253"></a>01253                 os &lt;&lt; <span class="stringliteral">"mu:"</span>&lt;&lt; ml.mu_const&lt;&lt;endl;
+<a name="l01254"></a>01254                 os &lt;&lt; <span class="stringliteral">"R:"</span> &lt;&lt; ml.epdf._R().to_mat() &lt;&lt;endl;
+<a name="l01255"></a>01255                 <span class="keywordflow">return</span> os;
+<a name="l01256"></a>01256         };
+<a name="l01257"></a>01257 
+<a name="l01258"></a>01258 }
+<a name="l01259"></a>01259 <span class="preprocessor">#endif //EF_H</span>
 </pre></div></div>
-<hr size="1"><address style="text-align: right;"><small>Generated on Tue Jun 2 10:11:00 2009 for mixpp by&nbsp;
+<hr size="1"><address style="text-align: right;"><small>Generated on Mon Jun 8 18:02:33 2009 for mixpp by&nbsp;
 <a href="http://www.doxygen.org/index.html">
 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>