Distributed revision of composite beliefs
Artificial Intelligence
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
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Readings in model-based diagnosis
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Artificial Intelligence Programming
Artificial Intelligence Programming
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UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
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AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A probabilistic model of plan recognition
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Focusing on probable diagnoses
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Reasoning MPE to multiply connected belief networks using message passing
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Equations for part-of-speech tagging
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
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SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Over the past several years Bayesian networks have been applied to a wide variety of problems. A central problem in applying Bayesian networks is that of finding one or more of the most probable instantiations of a network. In this paper we develop an efficient algorithm that incrementally enumerates the instantiations of a Bayesian network in decreasing order of probability. Such enumeration algorithms are applicable in a variety of applications ranging from medical expert systems to model-based diagnosis. Fundamentally, our algorithm is simply performing a lazy enumeration of the sorted list of all instantiations of the network. This insight leads to a very concise algorithm statement which is both easily understood and implemented. We show that for singly connected networks, our algorithm generates the next instantiation in time polynomial in the size of the network. The algorithm extends to arbitrary Bayesian networks using standard conditioning techniques. We empirically evaluate the enumeration algorithm and demonstrate its practicality.