## Bucket Elimination: a Unifying Framework for Structure-driven Inference (1998)

Venue: | Artificial Intelligence |

Citations: | 6 - 0 self |

### BibTeX

@ARTICLE{Dechter98bucketelimination:,

author = {Rina Dechter},

title = {Bucket Elimination: a Unifying Framework for Structure-driven Inference},

journal = {Artificial Intelligence},

year = {1998}

}

### OpenURL

### Abstract

Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many complex problem-solving and reasoning tasks. Algorithms such as directional-resolution for propositional satisfiability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination, for solving linear equalities and inequalities and dynamic programming for combinatorial optimization, can all be accommodated within the bucket elimination framework. Many probabilistic inference tasks can likewise be expressed as bucket-elimination algorithms. These include: belief updating, finding the most probable explanation and expected utility maximization. All these algorithms share the same performance guarantees; all are time and space exponential in the induced-width of the problem's interaction graph. While elimination strategies have extensive demands on memory, pure "conditioning" algorithms require only linear space. Conditioning is a generic name for a...