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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/demgauss.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 demgauss
+</title>
+</head>
+<body>
+<H1> demgauss
+</H1>
+<h2>
+Purpose
+</h2>
+Demonstrate sampling from Gaussian distributions.
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+demgauss
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+
+<p><CODE>demgauss</CODE> provides a simple illustration of the generation of
+data from Gaussian distributions. It first samples from a
+one-dimensional distribution using <CODE>randn</CODE>, and then plots a
+normalized histogram estimate of the distribution using <CODE>histp</CODE>
+together with the true density calculated using <CODE>gauss</CODE>.
+
+<p><CODE>demgauss</CODE> then demonstrates sampling from a Gaussian distribution
+in two dimensions. It creates a mean vector and a covariance matrix,
+and then plots contours of constant density using the function
+<CODE>gauss</CODE>. A sample of points drawn from this distribution, obtained
+using the function <CODE>gsamp</CODE>, is then superimposed on the contours.
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="gauss.htm">gauss</a></CODE>, <CODE><a href="gsamp.htm">gsamp</a></CODE>, <CODE><a href="histp.htm">histp</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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