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:
Learning for Neural Networks, Springer Verlag, New York (1996).
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.
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
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