Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
On the generation of alternative explanations with implications for belief revision
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
International Journal of Approximate Reasoning
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
A new algorithm for finding MAP assignments to belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Cost-Based Abduction and Linear Constraint Satisfaction
Cost-Based Abduction and Linear Constraint Satisfaction
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Probabilistic semantics for cost based abduction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Dynamic MAP calculations for abduction
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Reasoning MPE to multiply connected belief networks using message passing
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Abductive inference in bayesian networks: finding a partition of the explanation space
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Given a belief network with evidence, the task of finding the l most probable explanations (MPE) in the belief network is that of identifying and ordering the l most probable instantiations of the nonevidence nodes of the belief network. Although many approaches have been proposed for solving this problem, most work only for restricted topologies (i.e., singly connected belief networks). In this paper, we will present a new approach for finding l MPEs in an arbitrary belief network. First, we will present an algorithm for finding the MPE in a belief network. Then, we will present a linear time algorithm for finding the next MPE after finding the first MPE. And finally, we will discuss the problem of finding the MPE for a subset of variables of a belief network, and show that the problem can be efficiently solved by this approach.