Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual evidence Daniel@0: Daniel@0: Daniel@0: Daniel@0:

evidence Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Re-estimate hyperparameters using evidence approximation. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: [net] = evidence(net, x, t)
Daniel@0: [net, gamma, logev] = evidence(net, x, t, num)
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Daniel@0: Daniel@0: Daniel@0:

Daniel@0: Description Daniel@0:

Daniel@0: [net] = evidence(net, x, t) re-estimates the Daniel@0: hyperparameters alpha and beta by applying Bayesian Daniel@0: re-estimation formulae for num iterations. The hyperparameter Daniel@0: alpha can be a simple scalar associated with an isotropic prior Daniel@0: on the weights, or can be a vector in which each component is Daniel@0: associated with a group of weights as defined by the index Daniel@0: matrix in the net data structure. These more complex priors can Daniel@0: be set up for an MLP using mlpprior. Initial values for the iterative Daniel@0: re-estimation are taken from the network data structure net Daniel@0: passed as an input argument, while the return argument net Daniel@0: contains the re-estimated values. Daniel@0: Daniel@0:

[net, gamma, logev] = evidence(net, x, t, num) allows the re-estimation Daniel@0: formula to be applied for num cycles in which the re-estimated Daniel@0: values for the hyperparameters from each cycle are used to re-evaluate Daniel@0: the Hessian matrix for the next cycle. The return value gamma is Daniel@0: the number of well-determined parameters and logev is the log Daniel@0: of the evidence. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: mlpprior, netgrad, nethess, demev1, demard

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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