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1<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
2<html><head><meta http-equiv="Content-Type" content="text/html;charset=UTF-8">
3<title>mixpp: work/git/mixpp/bdm/stat/libBM.h File Reference</title>
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11      <li><a href="index.html"><span>Main&nbsp;Page</span></a></li>
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18<div class="contents">
19<h1>work/git/mixpp/bdm/stat/libBM.h File Reference</h1>Bayesian Models (bm) that use Bayes rule to learn from observations. <a href="#_details">More...</a>
20<p>
21<code>#include &lt;itpp/itbase.h&gt;</code><br>
22
23<p>
24<div class="dynheader">
25Include dependency graph for libBM.h:</div>
26<div class="dynsection">
27<p><center><img src="libBM_8h__incl.png" border="0" usemap="#work/git/mixpp/bdm/stat/libBM.h_map" alt=""></center>
28</div>
29
30<p>
31<div class="dynheader">
32This graph shows which files directly or indirectly include this file:</div>
33<div class="dynsection">
34<p><center><img src="libBM_8h__dep__incl.png" border="0" usemap="#work/git/mixpp/bdm/stat/libBM.hdep_map" alt=""></center>
35<map name="work/git/mixpp/bdm/stat/libBM.hdep_map">
36<area shape="rect" href="libFN_8h-source.html" title="work/git/mixpp/bdm/stat/libFN.h" alt="" coords="1151,84,1372,111"><area shape="rect" href="libEF_8h.html" title="Probability distributions for Exponential Family models." alt="" coords="303,84,521,111"><area shape="rect" href="emix_8h.html" title="Probability distributions for Mixtures of pdfs." alt="" coords="5,161,227,188"><area shape="rect" href="libDS_8h.html" title="Common DataSources." alt="" coords="649,84,873,111"><area shape="rect" href="loggers_8h.html" title="Loggers for storing results of experiments." alt="" coords="547,161,784,188"><area shape="rect" href="arx_8h.html" title="Bayesian Filtering for generalized autoregressive (ARX) model." alt="" coords="301,161,523,188"><area shape="rect" href="libKF_8h.html" title="Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions." alt="" coords="1071,161,1300,188"><area shape="rect" href="pmsm__mix_8cpp.html" title="Models for synchronous electric drive using IT++ and BDM." alt="" coords="760,316,1019,343"><area shape="rect" href="sim__var_8cpp.html" title="Models for synchronous electric drive using IT++ and BDM." alt="" coords="1353,239,1591,265"><area shape="rect" href="pmsm_8h-source.html" title="work/git/mixpp/pmsm/pmsm.h" alt="" coords="1375,161,1591,188"><area shape="rect" href="sim__var__arx_8cpp.html" title="Models for synchronous electric drive using IT++ and BDM." alt="" coords="279,239,545,265"><area shape="rect" href="ekf__templ_8h.html" title="Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions." alt="" coords="1065,239,1329,265"><area shape="rect" href="ekf__obj_8h.html" title="Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions." alt="" coords="620,239,1041,265"><area shape="rect" href="libPF_8h.html" title="Bayesian Filtering using stochastic sampling (Particle Filters)." alt="" coords="25,239,255,265"></map>
37</div>
38
39<p>
40<a href="libBM_8h-source.html">Go to the source code of this file.</a><table border="0" cellpadding="0" cellspacing="0">
41<tr><td></td></tr>
42<tr><td colspan="2"><br><h2>Classes</h2></td></tr>
43<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classRV.html">RV</a></td></tr>
44
45<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Class representing variables, most often random variables.  <a href="classRV.html#_details">More...</a><br></td></tr>
46<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classfnc.html">fnc</a></td></tr>
47
48<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Class representing function <img class="formulaInl" alt="$f(x)$" src="form_35.png"> of variable <img class="formulaInl" alt="$x$" src="form_28.png"> represented by <code>rv</code>.  <a href="classfnc.html#_details">More...</a><br></td></tr>
49<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classepdf.html">epdf</a></td></tr>
50
51<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Probability density function with numerical statistics, e.g. posterior density.  <a href="classepdf.html#_details">More...</a><br></td></tr>
52<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmpdf.html">mpdf</a></td></tr>
53
54<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Conditional probability density, e.g. modeling some dependencies.  <a href="classmpdf.html#_details">More...</a><br></td></tr>
55<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmepdf.html">mepdf</a></td></tr>
56
57<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Unconditional <a class="el" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>, allows using <a class="el" href="classepdf.html" title="Probability density function with numerical statistics, e.g. posterior density.">epdf</a> in the role of <a class="el" href="classmpdf.html" title="Conditional probability density, e.g. modeling some dependencies.">mpdf</a>.  <a href="classmepdf.html#_details">More...</a><br></td></tr>
58<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classDS.html">DS</a></td></tr>
59
60<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Abstract class for discrete-time sources of data.  <a href="classDS.html#_details">More...</a><br></td></tr>
61<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBM.html">BM</a></td></tr>
62
63<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities.  <a href="classBM.html#_details">More...</a><br></td></tr>
64<tr><td class="memItemLeft" nowrap align="right" valign="top">class &nbsp;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBMcond.html">BMcond</a></td></tr>
65
66<tr><td class="mdescLeft">&nbsp;</td><td class="mdescRight">Conditional Bayesian Filter.  <a href="classBMcond.html#_details">More...</a><br></td></tr>
67</table>
68<hr><a name="_details"></a><h2>Detailed Description</h2>
69Bayesian Models (bm) that use Bayes rule to learn from observations.
70<p>
71<dl class="author" compact><dt><b>Author:</b></dt><dd>Vaclav Smidl.</dd></dl>
72----------------------------------- BDM++ - C++ library for Bayesian Decision Making under Uncertainty<p>
73Using IT++ for numerical operations ----------------------------------- </div>
74<hr size="1"><address style="text-align: right;"><small>Generated on Sat Aug 16 17:22:04 2008 for mixpp by&nbsp;
75<a href="http://www.doxygen.org/index.html">
76<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.6 </small></address>
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