# rbfprior

## Purpose

Create Gaussian prior and output layer mask for RBF.

## Synopsis

    [mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)

## Description

`[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)` generates a vector `mask` that selects only the output layer weights. This is because most uses of RBF networks in a Bayesian context have fixed basis functions with the output layer as the only adjustable parameters. In particular, the Neuroscale output error function is designed to work only with this mask.

The return value `prior` is a data structure, with fields `prior.alpha` and `prior.index`, which specifies a Gaussian prior distribution for the network weights in an RBF network. The parameters `aw2` and `ab2` are all scalars and represent the regularization coefficients for two groups of parameters in the network corresponding to second-layer weights, and second-layer biases respectively. Then `prior.alpha` represents a column vector of length 2 containing the parameters, and `prior.index` is a matrix specifying which weights belong in each group. Each column has one element for each weight in the matrix, using the standard ordering as defined in `rbfpak`, and each element is 1 or 0 according to whether the weight is a member of the corresponding group or not.

## See Also

`rbf`, `rbferr`, `rbfgrad`, `evidence`

Pages: Index

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