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authorziejd22017-09-28 15:04:40 -0500
committerziejd22017-09-28 15:04:40 -0500
commit8070dc963753142bb86c4ed698d91fd623ed28e7 (patch)
treed0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mdnpost.htm
parent7cc31810d53176e805532b2789955f4eedbce6bb (diff)
downloadBNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
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+<html>
+<head>
+<title>
+Netlab Reference Manual mdnpost
+</title>
+</head>
+<body>
+<H1> mdnpost
+</H1>
+<h2>
+Purpose
+</h2>
+Computes the posterior probability for each MDN mixture component.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+post = mdnpost(mixparams, t)
+[post, a] = mdnpost(mixparams, t)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+<CODE>post = mdnpost(mixparams, t)</CODE> computes the posterior
+probability <CODE>p(j|t)</CODE> of each
+data vector in <CODE>t</CODE> under the Gaussian mixture model represented by the
+corresponding entries in <CODE>mixparams</CODE>. Each row of <CODE>t</CODE> represents a
+single vector.
+
+<p><CODE>[post, a] = mdnpost(mixparams, t)</CODE> also computes the activations
+<CODE>a</CODE> (i.e. the probability <CODE>p(t|j)</CODE> of the data conditioned on
+each component density) for a Gaussian mixture model.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mdnprob.htm">mdnprob</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+<p>David J Evans (1998)
+
+</body>
+</html>
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