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