[net] = evidence(net, x, t) [net, gamma, logev] = evidence(net, x, t, num)
[net] = evidence(net, x, t) re-estimates the
hyperparameters alpha and beta by applying Bayesian
re-estimation formulae for num iterations. The hyperparameter
alpha can be a simple scalar associated with an isotropic prior
on the weights, or can be a vector in which each component is
associated with a group of weights as defined by the index
matrix in the net data structure. These more complex priors can
be set up for an MLP using mlpprior. Initial values for the iterative
re-estimation are taken from the network data structure net
passed as an input argument, while the return argument net
contains the re-estimated values.
[net, gamma, logev] = evidence(net, x, t, num) allows the re-estimation
formula to be applied for num cycles in which the re-estimated
values for the hyperparameters from each cycle are used to re-evaluate
the Hessian matrix for the next cycle. The return value gamma is
the number of well-determined parameters and logev is the log
of the evidence.
mlpprior, netgrad, nethess, demev1, demardCopyright (c) Ian T Nabney (1996-9)