## 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 |
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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 |