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    8888</pre></div> Off-line estimation is implemented by method <div class="fragment"><pre class="fragment"> <span class="keywordtype">void</span> bayesB(mat D) 
    8989</pre></div><p> 
    90 As an intermediate product, the bayes rule computes marginal likelihood of the data records <img class="formulaInl" alt="$ f(D) $" src="form_95.png">. Numerical value of this quantity which is important e.g. for model selection can be obtained by calling method <code>_ll()</code>.<h2><a class="anchor" name="epdf"> 
     90As an intermediate product, the bayes rule computes marginal likelihood of the data records <img class="formulaInl" alt="$ f(D) $" src="form_103.png">. Numerical value of this quantity which is important e.g. for model selection can be obtained by calling method <code>_ll()</code>.<h2><a class="anchor" name="epdf"> 
    9191Getting results from BM</a></h2> 
    9292Class <code>BM</code> offers several ways how to obtain results: <ul> 
     
    111111The former class is an extension of mpdf that allows conditioning on a symbolic variable. Hence, when numerical results - such as samples - are required, numericla values of the condition must be provided. The names of methods of the <code>epdf</code> are used extended by suffix <code>cond</code>, i.e. <code>samplecond()</code>, <code>evallogcond()</code>, where <code>cond</code> precedes matrix estension, i.e. <code>samplecond_m()</code> and <code>evallogcond_m()</code>.<p> 
    112112The latter class is used to identify how symbolic variables are to be combined together. For example, consider the task of composition of pdfs via the chain rule: <p class="formulaDsp"> 
    113 <img class="formulaDsp" alt="\[ f(a,b,c) = f(a|b,c) f(b) f(c) \]" src="form_96.png"> 
     113<img class="formulaDsp" alt="\[ f(a,b,c) = f(a|b,c) f(b) f(c) \]" src="form_104.png"> 
    114114<p> 
    115  In our setup, <img class="formulaInl" alt="$ f(a|b,c) $" src="form_97.png"> is represented by an <code>mpdf</code> while <img class="formulaInl" alt="$ f(b) $" src="form_98.png"> and <img class="formulaInl" alt="$ f(c) $" src="form_99.png"> by two <code>epdfs</code>. We need to distinguish the latter two from each other and to deside in which order they should be added to the mpdf. This distinction is facilitated by the class <code>RV</code> which uniquely identify a random varibale.<p> 
     115 In our setup, <img class="formulaInl" alt="$ f(a|b,c) $" src="form_105.png"> is represented by an <code>mpdf</code> while <img class="formulaInl" alt="$ f(b) $" src="form_106.png"> and <img class="formulaInl" alt="$ f(c) $" src="form_107.png"> by two <code>epdfs</code>. We need to distinguish the latter two from each other and to deside in which order they should be added to the mpdf. This distinction is facilitated by the class <code>RV</code> which uniquely identify a random varibale.<p> 
    116116Therefore, each pdf keeps record on which RVs it represents; <code>epdf</code> needs to know only one <code>RV</code> stored in the attribute <code>rv</code>; <code>mpdf</code> needs to keep two <code>RVs</code>, one for variable on which it is defined (<code>rv</code>) and one for variable incondition which is stored in attribute <code>rvc</code>. </div> 
     117<<<<<<< HEAD:library/doc/html/intro.html 
    117118<hr size="1"><address style="text-align: right;"><small>Generated on Wed Jun 24 13:35:47 2009 for mixpp by&nbsp; 
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     120<hr size="1"><address style="text-align: right;"><small>Generated on Tue Jun 23 19:52:50 2009 for mixpp by&nbsp; 
     121>>>>>>> doc:library/doc/html/intro.html 
    118122<a href="http://www.doxygen.org/index.html"> 
    119123<img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.9 </small></address>