Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Applications of circumscription to formalizing common-sense knowledge
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A complexity analysis of assumption-based truth maintenance systems
Reason maintenance systems and their applications
Search-based methods to bound diagnostic probabilities in very large belief nets
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Selected papers of international conference on Fifth generation computer systems 92
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
An Introduction to Algorithms for Inference in Belief Nets
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
The use of conflicts in Searching Bayesian networks
The use of conflicts in Searching Bayesian networks
Probabilistic Horn abduction and Bayesian networks
Probabilistic Horn abduction and Bayesian networks
Goal oriented symbolic propagation in Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Belief functions and default reasoning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
On the relation between kappa calculus and probabilistic reasoning
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Localized partial evaluation of belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
The use of conflicts in searching Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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This paper provides a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency. The algorithm is most suited to the case where we have extreme (close to zero or one) probabilities, as is the case in many diagnostic situations where we are diagnosing systems that work most of the time, and for commonsense reasoning tasks where normality assumptions (allegedly) dominate. We give a characterisation of those cases where it works well, and discuss how well it can be expected to work on average.