## Abstract

Computing marginal probabilities in Bayes networks is a hard problem. Deterministic anytime approximation schemes accumulate the probability mass in a small number of value assignments to the network variables. Under certain assumptions, the probability mass in the assignments is sufficient to obtain a good approximation. Such methods are especially useful for highly connected networks, where the topology makes the exact algorithms intractable. Bayes networks often possess a fine independence structure not evident from the topology, but apparent in local conditional distributions. Independence-based (IB) assignments, originally proposed as a theory of abduction, take advantage of such independence, and thus contain fewer assigned variables - and more probability mass. We present several algorithms that use IB assignments for approximating marginal probabilities. Experimental results suggest that this approach is feasible for highly connected belief networks.

Original language | English |
---|---|

Pages (from-to) | 25-54 |

Number of pages | 30 |

Journal | International Journal of Approximate Reasoning |

Volume | 14 |

Issue number | 1 |

DOIs | |

State | Published - 1 Jan 1996 |

## Keywords

- Anytime algorithms
- Approximate belief updating
- Approximating marginal probabilities
- Bayesian belief networks
- Probabilistic reasoning

## ASJC Scopus subject areas

- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics