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Bucket Elimination: A Unifying Framework for Probabilistic Inference
Abstract
Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination-type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the aigorithms as a function of the problem's structure.
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Cited by
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Collective Classification in Network Data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T. (2008) · AI Magazine · doi:10.1609/aimag.v29i3.2157 · Cited by 3,293
Artificial Intelligence: A New Synthesis
(1998) · Elsevier eBooks · doi:10.1016/c2009-0-27773-7 · Cited by 1,163
A differential approach to inference in Bayesian networks
Darwiche, A. (2003) · Journal of the ACM · doi:10.1145/765568.765570 · Cited by 470
Metadata
- Authors
- Dechter, R.
- Venue
- Learning in Graphical Models
- Publisher
- Springer Netherlands
- Published
- 1998
- Pages
- 75-104
- OpenAlex
- W2141876475
- Cited by
- 452
- References
- 0
- FWCI
- 31.89
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Dechter, R. (1998). Bucket Elimination: A Unifying Framework for Probabilistic Inference. Learning in Graphical Models. https://doi.org/10.1007/978-94-011-5014-9_4
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