Approximate inference for infinite contingent Bayesian networks

Brian Milch, Bhaskara Marthi, David Sontag, Stuart Russell, Daniel L. Ong, Andrey Kolobov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In many practical problems-from tracking aircraft based on radar data to building a bibliographic database based on citation lists-we want to reason about an unbounded number of unseen objects with unknown relations among them. Bayesian networks, which define a fixed dependency structure on a finite set of variables, are not the ideal representation language for this task. This paper introduces contingent Bayesian networks (CBNs), which represent uncertainty about dependencies by labeling each edge with a condition under which it is active. A CBN may contain cycles and have infinitely many variables. Nevertheless, we give general conditions under which such a CBN defines a unique joint distribution over its variables. We also present a likelihood weighting algorithm that performs approximate inference in finite time per sampling step on any CBN that satisfies these conditions.

Original languageEnglish (US)
Title of host publicationAISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics
Pages238-245
Number of pages8
StatePublished - 2005
Event10th International Workshop on Artificial Intelligence and Statistics, AISTATS 2005 - Hastings, Christ Church, Barbados
Duration: Jan 6 2005Jan 8 2005

Other

Other10th International Workshop on Artificial Intelligence and Statistics, AISTATS 2005
CountryBarbados
CityHastings, Christ Church
Period1/6/051/8/05

Fingerprint

Bayesian networks
Bayesian Networks
Edge Labeling
Citations
Joint Distribution
Labeling
Radar
Weighting
Aircraft
Finite Set
Likelihood
Sampling
Uncertainty
Cycle
Unknown

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

Cite this

Milch, B., Marthi, B., Sontag, D., Russell, S., Ong, D. L., & Kolobov, A. (2005). Approximate inference for infinite contingent Bayesian networks. In AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (pp. 238-245)

Approximate inference for infinite contingent Bayesian networks. / Milch, Brian; Marthi, Bhaskara; Sontag, David; Russell, Stuart; Ong, Daniel L.; Kolobov, Andrey.

AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005. p. 238-245.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Milch, B, Marthi, B, Sontag, D, Russell, S, Ong, DL & Kolobov, A 2005, Approximate inference for infinite contingent Bayesian networks. in AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. pp. 238-245, 10th International Workshop on Artificial Intelligence and Statistics, AISTATS 2005, Hastings, Christ Church, Barbados, 1/6/05.
Milch B, Marthi B, Sontag D, Russell S, Ong DL, Kolobov A. Approximate inference for infinite contingent Bayesian networks. In AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005. p. 238-245
Milch, Brian ; Marthi, Bhaskara ; Sontag, David ; Russell, Stuart ; Ong, Daniel L. ; Kolobov, Andrey. / Approximate inference for infinite contingent Bayesian networks. AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics. 2005. pp. 238-245
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