Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An analysis of first-order logics of probability
Artificial Intelligence
Do the right thing: studies in limited rationality
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The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Probabilistic reasoning for complex systems
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IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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Machine Learning
Machine Learning
Probabilistic reasoning with answer sets
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First-order probabilistic inference
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Dynamic probabilistic relational models
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Compiling relational Bayesian networks for exact inference
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A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete possible worlds underlying any given knowledge base. The simplicity of the approach and its lazy construction of possible worlds make it possible to consider quite expressive languages. In particular, we consider two extensions to the basic relational probability models (RPMs) defined by Koller and Pfeffer, both of which have caused difficulties for exact algorithms. The first extension deals with uncertainty about relations among objects, where MCMC samples over relational structures. The second extension deals with uncertainty about the identity of individuals, where MCMC samples over sets of equivalence classes of objects. In both cases, we identify types of probability distributions that allow local decomposition of inference while encoding possible domains in a plausible way. We apply our algorithms to simple examples and show that the MCMC approach scales well.