Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
On the hardness of approximate reasoning
Artificial Intelligence
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Inference in multilayer networks via large deviation bounds
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probability Bounds for Goal Directed Queries in Bayesian Networks
IEEE Transactions on Knowledge and Data Engineering
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Journal of the ACM (JACM)
Exploiting graph cutsets for sampling-based approximations in bayesian networks
Exploiting graph cutsets for sampling-based approximations in bayesian networks
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
An anytime scheme for bounding posterior beliefs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
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AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Large deviation methods for approximate probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Context-specific approximation in probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Error bounds between marginal probabilities and beliefs of loopy belief propagation algorithm
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
An empirical study of w-cutset sampling for bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
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The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime manner. The scheme uses the cutset conditioning principle to tighten existing bounding schemes and to facilitate anytime behavior, utilizing a fixed number of cutset tuples. The accuracy of the bounds improves as the number of used cutset tuples increases and so does the computation time. We demonstrate empirically the value of our scheme for bounding posterior marginals and probability of evidence using a variant of the bound propagation algorithm as a plug-in scheme.