A credal network associates sets of probability distributions with directed acyclic graphs. Under strong independence assumptions, inference with credal networks is equivalent to a signomial program under linear constraints, a problem that is NP-hard even for categorical variables and polytree models. We describe

This paper presents alternatives to Lavine's algorithm, currently the most popular method for calculation of expectation bounds induced by sets of probability distributions. The White-Snow algorithm is first analyzed and demonstrated to be superior to Lavine's algorithm in a variety of situations. The calculation of posterior bounds is then reduced to a fractional programming problem. From the unifying perspective of fractional programming, Lavine's algorithm is identical to Dinkelbach's algorithm, and the White-Snow algorithm is essentially identical to the Charnes-Cooper transformation. A novel algorithm for expectation bounds is given for the situation where both prior and likelihood functions are specified as convex sets of distributions. 1 Introduction This paper presents alternatives to Lavine's algorithm, currently the most popular method for calculation of expectation bounds induced by sets of probability distributions. Models based on convex sets of distributions have been ...