📄 evidence.htm
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<html><head><title>Netlab Reference Manual evidence</title></head><body><H1> evidence</H1><h2>Purpose</h2>Re-estimate hyperparameters using evidence approximation.<p><h2>Synopsis</h2><PRE>[net] = evidence(net, x, t)[net, gamma, logev] = evidence(net, x, t, num)</PRE><p><h2>Description</h2><CODE>[net] = evidence(net, x, t)</CODE> re-estimates thehyperparameters <CODE>alpha</CODE> and <CODE>beta</CODE> by applying Bayesianre-estimation formulae for <CODE>num</CODE> iterations. The hyperparameter<CODE>alpha</CODE> can be a simple scalar associated with an isotropic prioron the weights, or can be a vector in which each component isassociated with a group of weights as defined by the <CODE>index</CODE>matrix in the <CODE>net</CODE> data structure. These more complex priors canbe set up for an MLP using <CODE>mlpprior</CODE>. Initial values for the iterativere-estimation are taken from the network data structure <CODE>net</CODE>passed as an input argument, while the return argument <CODE>net</CODE>contains the re-estimated values.<p><CODE>[net, gamma, logev] = evidence(net, x, t, num)</CODE> allows the re-estimation formula to be applied for <CODE>num</CODE> cycles in which the re-estimatedvalues for the hyperparameters from each cycle are used to re-evaluatethe Hessian matrix for the next cycle. The return value <CODE>gamma</CODE> isthe number of well-determined parameters and <CODE>logev</CODE> is the logof the evidence.<p><h2>See Also</h2><CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="nethess.htm">nethess</a></CODE>, <CODE><a href="demev1.htm">demev1</a></CODE>, <CODE><a href="demard.htm">demard</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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