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