# fevbayes

## Purpose

Evaluate Bayesian regularisation for network forward propagation.

## Synopsis

    extra = fevbayes(net, y, a, x, t, x_test)
    [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess)

## Description

`extra = fevbayes(net, y, a, x, t, x_test)` takes a network data structure `net` together with a set of hidden unit activations `a` from test inputs `x_test`, training data inputs `x` and `t` and outputs a matrix of extra information `extra` that consists of error bars (variance) for a regression problem or moderated outputs for a classification problem. The optional argument (and return value) `invhess` is the inverse of the network Hessian computed on the training data inputs and targets. Passing it in avoids recomputing it, which can be a significant saving for large training sets.

This is called by network-specific functions such as `mlpevfwd` which are needed since the return values (predictions and hidden unit activations) for different network types are in different orders (for good reasons).

## See Also

`mlpevfwd`, `rbfevfwd`, `glmevfwd`

Pages: Index

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