When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…

Summarizes a simple design algorithm which identifies nested loops of equations which must be solved by trial-and-error methods. The algorithm is designed to minimize such loops, provides guidance to the selection of variables, and delineates the order in which systems of equations are to be solved. Examples are included. (TW)