# glminit

## Purpose

Initialise the weights in a generalized linear model.

## Synopsis

    net = glminit(net, prior)

## Description

`net = glminit(net, prior)` takes a generalized linear model `net` and sets the weights and biases by sampling from a Gaussian distribution. If `prior` is a scalar, then all of the parameters (weights and biases) are sampled from a single isotropic Gaussian with inverse variance equal to `prior`. If `prior` is a data structure similar to that in `mlpprior` but for a single layer of weights, then the parameters are sampled from multiple Gaussians according to their groupings (defined by the `index` field) with corresponding variances (defined by the `alpha` field).

## See Also

`glm`, `glmpak`, `glmunpak`, `mlpinit`, `mlpprior`

Pages: Index

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