Computing posterior upper expectations

This article investigates the computation of posterior upper expectations induced by imprecise probabilities, with emphasis on the effects of irrelevance and independence judgements. Algorithms that handle imprecise priors and imprecise likelihoods are reviewed, and a new result on the limiting divergence of posterior upper probabilities is presented. Algorithms that handle irrelevance and independence relations in multivariate models are analyzed through graphical representations, inspired by the popular Bayesian network model. © 2000 Elsevier Science Inc. All rights reserved.