In *Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics*, pages 238–245, 2005.

Paper abstract bibtex 1 download

Paper abstract bibtex 1 download

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.

@inproceedings{MilchEtAl_aistats05, title = {Approximate Inference for Infinite Contingent {B}ayesian Networks}, author = {Brian Milch and Bhaskara Marthi and David Sontag and Stuart Russell and Daniel L. Ong and Andrey Kolobov}, booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics}, year = {2005}, pages = {238--245}, keywords = {Machine learning}, url_Paper = {http://people.csail.mit.edu/dsontag/papers/MilchEtAl_AIStats05.pdf}, 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.} } % editor = {Robert G. Cowell and Zoubin Ghahramani}, % publisher = {Society for Artificial Intelligence and Statistics},

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