# gbayes

## Purpose

Evaluate gradient of Bayesian error function for network.

## Synopsis

    g = gbayes(net, gdata)
    [g, gdata, gprior] = gbayes(net, gdata)

## Description

`g = gbayes(net, gdata)` takes a network data structure `net` together the data contribution to the error gradient for a set of inputs and targets. It returns the regularised error gradient using any zero mean Gaussian priors on the weights defined in `net`. In addition, if a `mask` is defined in `net`, then the entries in `g` that correspond to weights with a 0 in the mask are removed.

`[g, gdata, gprior] = gbayes(net, gdata)` additionally returns the data and prior components of the error.

## See Also

`errbayes`, `glmgrad`, `mlpgrad`, `rbfgrad`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)