Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Blocking Gibbs sampling in very large probabilistic expert systems
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Introduction to Monte Carlo methods
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A general algorithm for approximate inference and its application to hybrid bayes nets
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical evaluation of approximation algorithms for probabilistic decoding
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
HUGS: combining exact inference and Gibbs sampling in junction trees
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
An anytime scheme for bounding posterior beliefs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Active tuples-based scheme for bounding posterior beliefs
Journal of Artificial Intelligence Research
An empirical study of w-cutset sampling for bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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The paper presents a new sampling methodology for Bayesian networks called cutset sampling that samples only a subset of the variables and applies exact inference for the others. We show that this approach can be implemented efficiently when the sampled variables constitute a cycle-cutset for the Bayesian network and otherwise it is exponential in the induced-width of the network's graph, whose sampled variables are removed. Cutset sampling is an instance of the well known Rao-Blakwellisation technique for variance reduction investigated in [5, 2, 16]. Moreover, the proposed scheme extends standard sampling methods to non-ergodic networks with ergodic subspaces. Our empirical results confirm those expectations and show that cycle cutset sampling is superior to Gibbs sampling for a variety of benchmarks, yielding a simple, yet powerful sampling scheme.