# demhmc2

## Purpose

Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.

## Synopsis

    demhmc2

## Description

The problem consists of one input variable `x` and one target variable `t` with data generated by sampling `x` at equal intervals and then generating target data by computing `sin(2*pi*x)` and adding Gaussian noise. The model is a 2-layer network with linear outputs, and the hybrid Monte Carlo algorithm (without persistence) is used to sample from the posterior distribution of the weights. The graph shows the underlying function, 100 samples from the function given by the posterior distribution of the weights, and the average prediction (weighted by the posterior probabilities).

## See Also

`demhmc3`, `hmc`, `mlp`, `mlperr`, `mlpgrad`

Pages: Index

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