Changeset 323 for doc/html/intro.html
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r312 r323 88 88 </pre></div> Off-line estimation is implemented by method <div class="fragment"><pre class="fragment"> <span class="keywordtype">void</span> bayesB(mat D) 89 89 </pre></div><p> 90 As an intermediate product, the bayes rule computes marginal likelihood of the data records <img class="formulaInl" alt="$ f(D) $" src="form_ 87.png">. Numerical value of this quantity which is important e.g. for model selection can be obtained by calling method <code>_ll()</code>.<h2><a class="anchor" name="epdf">90 As an intermediate product, the bayes rule computes marginal likelihood of the data records <img class="formulaInl" alt="$ 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"> 91 91 Getting results from BM</a></h2> 92 92 Class <code>BM</code> offers several ways how to obtain results: <ul> … … 111 111 The 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> 112 112 The 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_ 88.png">113 <img class="formulaDsp" alt="\[ f(a,b,c) = f(a|b,c) f(b) f(c) \]" src="form_96.png"> 114 114 <p> 115 In our setup, <img class="formulaInl" alt="$ f(a|b,c) $" src="form_ 89.png"> is represented by an <code>mpdf</code> while <img class="formulaInl" alt="$ f(b) $" src="form_90.png"> and <img class="formulaInl" alt="$ f(c) $" src="form_91.png"> by two <code>epdfs</code>. We need to distinguish the latter two from each other and to deside in which order they should be added to the mpdf. This distinction is facilitated by the class <code>RV</code> which uniquely identify a random varibale.<p>115 In our setup, <img class="formulaInl" alt="$ f(a|b,c) $" src="form_97.png"> is represented by an <code>mpdf</code> while <img class="formulaInl" alt="$ 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> 116 116 Therefore, 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 <hr size="1"><address style="text-align: right;"><small>Generated on Thu Apr 9 14:33:202009 for mixpp by 117 <hr size="1"><address style="text-align: right;"><small>Generated on Thu Apr 23 21:06:43 2009 for mixpp by 118 118 <a href="http://www.doxygen.org/index.html"> 119 119 <img src="doxygen.png" alt="doxygen" align="middle" border="0"></a> 1.5.8 </small></address>