# Method home for mlp-mc-1

In this method a fully connected multilayer perceptron neural network with
a single hidden layer of hyperbolic tangent units is trained using Bayesian
learning implemented via Markov Chain Monte Carlo (MCMC) techniques.
For a more detailed theoretical account of the method, refer to Radford Neal:
*Bayesian
Learning for Neural Networks*, Springer Verlag, New York (1996).
## Software

The software used to implement this method is a very general
package of Markov Chain sampling techniques for Bayesian learning
in neural networks developed by Radford Neal.
The definition of the **mlp-mc-1** method is given in a postscript document which also includes a sample script (written
for the `csh` shell) to be used in conjunction with the software.

## Results

Directory listing of the results available for the
**mlp-mc-1** method. Put the desired files in the appropriate methods
directory in your delve hierarchy and uncompress them with using the
`"gunzip *.gz"` command and untar them using `"tar -xvf
*.tar"`.
## Related References

A similar method was considered in: Carl Edward Rasmussen "A Practical
Monte Carlo Implementation of Bayesian Learning", *Advances in
Neural Information Processing Systems 8*, eds. D. S. Touretzky,
M. C. Mozer, M. E. Hasselmo, MIT Press, 1996.

Last Updated by Carl Edward
Rasmussen, September 16, 1996