## Abstract

Credal networks lift the precise probability assumption of Bayesian networks, enabling a richer representation of uncertainty in the form of closed convex sets of probability measures. The increase in expressiveness comes at the expense of higher computational costs. In this paper we present a new algorithm which is an extension of the wellknown variable elimination algorithm for computing posterior inferences in extensively specified credal networks. The algorithm efficiency is empirically shown to outperform a state-of-the-art algorithm. We then provide the first fully polynomial time approximation scheme for inference in credal networks with bounded treewidth and number of states per variable.

Original language | English |
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Title of host publication | ISIPTA 2011 - Proceedings of the 7th International Symposium on Imprecise Probability |

Subtitle of host publication | Theories and Applications |

Pages | 277-286 |

Number of pages | 10 |

Publication status | Published - 1 Dec 2011 |

Externally published | Yes |

Event | 7th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2011 - Innsbruck, Austria Duration: 25 Jul 2011 → 28 Jul 2011 |

### Conference

Conference | 7th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2011 |
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Country/Territory | Austria |

City | Innsbruck |

Period | 25/07/11 → 28/07/11 |

## Keywords

- Approximation scheme
- Credal networks
- Probabilistic graphical models
- Valuation algebra