83 lines
3.6 KiB
HTML
83 lines
3.6 KiB
HTML
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<title>Description of recursive_moments</title>
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<div><a href="../index.html">Home</a> > <a href="index.html">.</a> > recursive_moments.m</div>
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<!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td>
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<td align="right"><a href="index.html">Index for . <img alt=">" border="0" src="../right.png"></a></td></tr></table>-->
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<h1>recursive_moments
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</h1>
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="box"><strong>stephane.adjemian@cepremap.cnrs.fr [02-03-2005]</strong></div>
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<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="box"><strong>function [mu,sigma] = recursive_moments(m0,s0,data,offset) </strong></div>
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<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="fragment"><pre class="comment"> stephane.adjemian@cepremap.cnrs.fr [02-03-2005]
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m0 :: the initial mean, assumed to be a n*1 vector.
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s0 :: the initial variance, assumed to be a n*n matrix.
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data :: T*n matrix.
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mu :: the sample mean, a n*1 vector.
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sigma :: the sample covariance matrix, a n*n matrix.
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--> TODO: dll</pre></div>
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<!-- crossreference -->
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<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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This function calls:
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<ul style="list-style-image:url(../matlabicon.gif)">
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</ul>
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This function is called by:
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<ul style="list-style-image:url(../matlabicon.gif)">
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</ul>
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<!-- crossreference -->
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<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
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<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function [mu,sigma] = recursive_moments(m0,s0,data,offset)</a>
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0002 <span class="comment">% stephane.adjemian@cepremap.cnrs.fr [02-03-2005]</span>
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0003 <span class="comment">%</span>
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0004 <span class="comment">% m0 :: the initial mean, assumed to be a n*1 vector.</span>
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0005 <span class="comment">% s0 :: the initial variance, assumed to be a n*n matrix.</span>
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0006 <span class="comment">% data :: T*n matrix.</span>
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0007 <span class="comment">%</span>
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0008 <span class="comment">% mu :: the sample mean, a n*1 vector.</span>
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0009 <span class="comment">% sigma :: the sample covariance matrix, a n*n matrix.</span>
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0010 <span class="comment">%</span>
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0011 <span class="comment">% --> TODO: dll</span>
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0012 T = size(data,1);
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0013 n = size(data,2);
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0014
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0015 <span class="keyword">for</span> t = 1:T
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0016 tt = t+offset;
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0017 m1 = m0 + (1/tt)*(data(t,:)'-m0);
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0018 s1 = s0 + (1/tt)*(data(t,:)'*data(t,:)-m1*m1'-s0) + ((tt-1)/tt)*(m0*m0'-m1*m1');
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0019 m0 = m1;
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0020 s0 = s1;
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0021 <span class="keyword">end</span>
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0022
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0023 mu = m1;
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0024 sigma = s1;
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0025
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0026
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0027
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0028</pre></div>
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<hr><address>Generated on Fri 16-Jun-2006 09:09:06 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/">m2html</a></strong> © 2003</address>
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