    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, biases, etc.).

       sample-noise

           Does Gibbs sampling for the noise variances.

       sample-lower-hyper

           Does Gibbs sampling for all lower-level hyperparameters.

       rgrid--upper-hyper [ stepsize ]

           Does random-grid Metropolis updates (one at a time) for
           the logs of all upper-level hyperparameters, in "precision"
           form.  The default stepsize is 0.1.

       sample-lower-noise

           Does Gibbs sampling for all lower-level noise variances.

       rgrid-upper-noise [ stepsize ]

           Does random-grid Metropolis updates (one at a time) for the
           logs of all upper-level hyperameters controlling noise
           variances, in "precison" form.  The default stepsize is 0.1.

       sample-sigmas

           Does the equivalent of both sample-hyper and sample-noise.

       sample-lower-sigmas

           Does the equivalent of both sample-lower-hyper and
           sample-lower-noise.

       sample-upper-sigmas

           Does the equivalent of both sample-upper-hyper and
           sample-upper-noise.

    An "upper-level" hyperparameter is one that controls the distribution
    of lower-level hyperparameters or noise variances (which may be either
    explicit or implicit).  The "lower-level" hyperparameters directly
    control the distributions of weights.  Looked at another way, the
    lower-level hyperparameters are the ones at the bottom level of the
    hierarchy, or for which all lower-level hyperparameters have
    degenerate distributions concentrated on the value of the higher-level
    hyperparameter.

    The random grid metropolis updates done with the above commands record
    information (eg, rejection rate) for later display in the same way as
    generic rgrid-met-1 updates.

    When coupling is being done, upper-level hyperparameters should be
    updated only with random-grid updates; Gibbs sampling for these
    upper-level hyperparamters will desynchronize the random number
    streams (because of the way it is implemented using ARS), preventing
    coalescence.  Lower-level hyperparameters can be updated with Gibbs
    sampling, and they will exactly coalesce once the parameters they
    control and the upper-level hyperparameters that control them have
    exactly coalesced.

    Default stepsizes for updates of parameters (weights, biases, etc.) by
    the generic Markov chain operations 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) 1995-2004 by Radford M. Neal