# demev1

## Purpose

Demonstrate Bayesian regression for the MLP.

## Synopsis

    demev1

## Description

The problem consists an input variable `x` which sampled from a Gaussian distribution, and a target variable `t` generated by computing `sin(2*pi*x)` and adding Gaussian noise. A 2-layer network with linear outputs is trained by minimizing a sum-of-squares error function with isotropic Gaussian regularizer, using the scaled conjugate gradient optimizer. The hyperparameters `alpha` and `beta` are re-estimated using the function `evidence`. A graph is plotted of the original function, the training data, the trained network function, and the error bars.

## See Also

`evidence`, `mlp`, `scg`, `demard`, `demmlp1`

Pages: Index

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