NET-MC:  Do Markov chain simulation to sample networks.

The net-mc program is the specialization of xxx-mc to the task of
sampling from the posterior distribution for a neural network model,
or from the prior distribution, if no training set is specified.  See
xxx-mc.doc for the generic features of this program.

The following applications-specific sampling procedures are implemented:

   sample-hyper   Does Gibbs sampling for the hyperparameters controlling
                  the distributions of parameters (weights, etc.).

   sample-noise   Does Gibbs sampling for the noise variances.

   sample-sigmas  Does both sample-hyper and sample-noise.

Default stepsizes are set by a complicated heuristic procedure that is
described in Appendix A of the thesis.

Tempering methods and Annealed Importance sampling are supported.  The
effect of running at an inverse temperature other than one is to
multiply the likelihood part of the energy by that amount.  At inverse
temperature zero, the distribution is simply the prior for the
hyperparameters and weights.  The marginal likelihood for a model can
be found using Annealed Importance Sampling, since the log likelihood
part of the energy has all the appropriate normalizing constants.

            Copyright (c) 1995, 1998 by Radford M. Neal