Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge representation and inference in similarity networks and Bayesian multinets
Artificial Intelligence
Recursive hashing functions for n-grams
ACM Transactions on Information Systems (TOIS)
Efficient Bayesian parameter estimation in large discrete domains
Proceedings of the 1998 conference on Advances in neural information processing systems II
Matching records in a national medical patient index
Communications of the ACM
Machine Learning
A one pass decoder design for large vocabulary recognition
HLT '94 Proceedings of the workshop on Human Language Technology
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
On Directed and Undirected Propagation Algorithms for Bayesian Networks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Probability and equality: a probabilistic model of identity uncertainty
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
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In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of all postal codes, or the set of all credit card numbers. We cannot just have big tables of the conditional probabilities, but need compact representations. We provide an inference algorithm, based on variable elimination, for belief networks containing both large domain and normal discrete random variables. We use intensional (i.e., in terms of procedures) and extensional (in terms of listing the elements) representations of conditional probabilities and of the intermediate factors.