# mlpprior

## Purpose

Create Gaussian prior for mlp.

## Synopsis

    prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)

## Description

`prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)` generates a data structure `prior`, with fields `prior.alpha` and `prior.index`, which specifies a Gaussian prior distribution for the network weights in a two-layer feedforward network. Two different cases are possible. In the first case, `aw1`, `ab1`, `aw2` and `ab2` are all scalars and represent the regularization coefficients for four groups of parameters in the network corresponding to first-layer weights, first-layer biases, second-layer weights, and second-layer biases respectively. Then `prior.alpha` represents a column vector of length 4 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 `mlppak`, and each element is 1 or 0 according to whether the weight is a member of the corresponding group or not. In the second case the parameter `aw1` is a vector of length equal to the number of inputs in the network, and the corresponding matrix `prior.index` now partitions the first-layer weights into groups corresponding to the weights fanning out of each input unit. This prior is appropriate for the technique of automatic relevance determination.

## See Also

`mlp`, `mlperr`, `mlpgrad`, `evidence`

Pages: Index

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