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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gmmprob.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-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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diff --git a/sourcecodes/bnt-master/nethelp3.3/gmmprob.htm b/sourcecodes/bnt-master/nethelp3.3/gmmprob.htm new file mode 100644 index 00000000..acc3fb65 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gmmprob.htm @@ -0,0 +1,44 @@ +<html> +<head> +<title> +Netlab Reference Manual gmmprob +</title> +</head> +<body> +<H1> gmmprob +</H1> +<h2> +Purpose +</h2> +Computes the data probability for a Gaussian mixture model. + +<p><h2> +Synopsis +</h2> +<PRE> + +function prob = gmmprob(mix, x) +</PRE> + + +<p><h2> +Description +</h2> + +This function computes the unconditional data +density <CODE>p(x)</CODE> for a Gaussian mixture model. The data structure +<CODE>mix</CODE> defines the mixture model, while the matrix <CODE>x</CODE> contains +the data vectors. Each row of <CODE>x</CODE> represents a single vector. + +<p><h2> +See Also +</h2> +<CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE>, <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file |