    BVG-MC:  Do Markov chain simulation to sample from a bivariate Gaussian.

    The bvg-mc program is the specialization of xxx-mc to the task of
    sampling from a bivariate Gaussian.  See xxx-mc.doc for the generic
    features of this program.

    The following applications-specific sampling procedures are implemented:

        gibbs          Does a Gibbs sampling update for all replications
                       of the bivariate Gaussian.  A update consists of
                       sampling first from the conditional distribution for
                       the first component, and then from the conditional
                       distribution for the second component.

        gibbs0 alpha   Does an Adler-style overrelaxed update for the first
                       component (separately for all replications).  The
                       new value of the offset of the component from its
                       conditional mean is alpha times the old offset plus
                       Gaussian noise of variance 1-alpha^2 times the
                       conditional variance.

        gibbs1 alpha   Does an Adler-style overrelaxed update for the second
                       component.

    To do Adler-style successive overrelaxation, gibbs0 and gibbs1 should
    be done alternately.

    The inverse temperature used in tempering methods is interpreted in
    the standard way, as a power to raise the (unnormalized) probability
    density to, or equivalently, a factor to multiply the energy by.

    The default dynamical stepsizes are all set to one, except that they
    are appropriately scaled during tempering.

                Copyright (c) 1995-2004 by Radford M. Neal