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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual evidence
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5 </title>
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6 </head>
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7 <body>
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8 <H1> evidence
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Re-estimate hyperparameters using evidence approximation.
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14
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15 <p><h2>
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16 Synopsis
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17 </h2>
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18 <PRE>
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19 [net] = evidence(net, x, t)
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20 [net, gamma, logev] = evidence(net, x, t, num)
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21 </PRE>
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22
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23
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24 <p><h2>
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25 Description
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26 </h2>
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27 <CODE>[net] = evidence(net, x, t)</CODE> re-estimates the
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28 hyperparameters <CODE>alpha</CODE> and <CODE>beta</CODE> by applying Bayesian
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29 re-estimation formulae for <CODE>num</CODE> iterations. The hyperparameter
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30 <CODE>alpha</CODE> can be a simple scalar associated with an isotropic prior
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31 on the weights, or can be a vector in which each component is
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32 associated with a group of weights as defined by the <CODE>index</CODE>
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33 matrix in the <CODE>net</CODE> data structure. These more complex priors can
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34 be set up for an MLP using <CODE>mlpprior</CODE>. Initial values for the iterative
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35 re-estimation are taken from the network data structure <CODE>net</CODE>
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36 passed as an input argument, while the return argument <CODE>net</CODE>
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37 contains the re-estimated values.
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38
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39 <p><CODE>[net, gamma, logev] = evidence(net, x, t, num)</CODE> allows the re-estimation
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40 formula to be applied for <CODE>num</CODE> cycles in which the re-estimated
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41 values for the hyperparameters from each cycle are used to re-evaluate
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42 the Hessian matrix for the next cycle. The return value <CODE>gamma</CODE> is
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43 the number of well-determined parameters and <CODE>logev</CODE> is the log
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44 of the evidence.
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45
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46 <p><h2>
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47 See Also
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48 </h2>
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49 <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>
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50 <b>Pages:</b>
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51 <a href="index.htm">Index</a>
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52 <hr>
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53 <p>Copyright (c) Ian T Nabney (1996-9)
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54
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55
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56 </body>
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57 </html> |
