Abstract
A credal network associates a directed acyclic graph with a collection of sets of probability measures; it offers a compact representation for sets of multivariate distributions. In this paper we present a new algorithm for inference in credal networks based on an integer programming reformulation. We are concerned with computation of lower/upper probabilities for a variable in a given credal network. Experiments reported in this paper indicate that this new algorithm has better performance than existing ones for some important classes of networks.
Original language | English |
---|---|
Title of host publication | International Symposium on Imprecise Probability: Theories and Applications (ISIPTA) |
Publisher | SIPTA |
Pages | 145-154 |
Number of pages | 10 |
Publication status | Published - 2007 |