TY - GEN

T1 - Approximate inference for infinite contingent Bayesian networks

AU - Milch, Brian

AU - Marthi, Bhaskara

AU - Sontag, David

AU - Russell, Stuart

AU - Ong, Daniel L.

AU - Kolobov, Andrey

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84862596534&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862596534&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84862596534

SN - 097273581X

SN - 9780972735810

T3 - AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

SP - 238

EP - 245

BT - AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics

T2 - 10th International Workshop on Artificial Intelligence and Statistics, AISTATS 2005

Y2 - 6 January 2005 through 8 January 2005

ER -