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