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<html><head><title>Netlab Reference Manual demprior</title></head><body><H1> demprior</H1><h2>Purpose</h2>Demonstrate sampling from a multi-parameter Gaussian prior.<p><h2>Synopsis</h2><PRE>demprior</PRE><p><h2>Description</h2>This function plots the functions represented by a multi-layer perceptronnetwork when the weights are set to values drawn from a Gaussian priordistribution. The parameters <CODE>aw1</CODE>, <CODE>ab1</CODE> <CODE>aw2</CODE> and <CODE>ab2</CODE> control the inverse variances of the first-layer weights, the hidden unit biases, the second-layer weights and the output unit biases respectively. Their values can be adjusted on a logarithmic scale using the sliders, or by typing values into the text boxes and pressing the return key. <p><h2>See Also</h2><CODE><a href="mlp.htm">mlp</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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