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<html>
<head>
<title>
Netlab Reference Manual glminit
</title>
</head>
<body>
<H1> glminit
</H1>
<h2>
Purpose
</h2>
Initialise the weights in a generalized linear model.
<p><h2>
Synopsis
</h2>
<PRE>
net = glminit(net, prior)
</PRE>
<p><h2>
Description
</h2>
<p><CODE>net = glminit(net, prior)</CODE> takes a generalized linear model
<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
(weights and biases) are sampled from a single isotropic Gaussian with
inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
structure similar to that in <CODE>mlpprior</CODE> but for a single layer of
weights, then the parameters
are sampled from multiple Gaussians according to their groupings
(defined by the <CODE>index</CODE> field) with corresponding variances
(defined by the <CODE>alpha</CODE> field).
<p><h2>
See Also
</h2>
<CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="mlpinit.htm">mlpinit</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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