Random generation of combinatorial structures from a uniform
Theoretical Computer Science
How hard is it to marry at random? (On the approximation of the permanent)
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Approximate counting, uniform generation and rapidly mixing Markov chains
Information and Computation
SIAM Journal on Computing
Architectures and approximation algorithms for probabilistic expert systems
Architectures and approximation algorithms for probabilistic expert systems
Reformulating inference problems through selective conditioning
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Bayes networks for estimating the number of solutions of constraint networks
Annals of Mathematics and Artificial Intelligence
Probabilistic Methods for Financial and Marketing Informatics
Probabilistic Methods for Financial and Marketing Informatics
A Bayesian approach to protein model quality assessment
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Journal of Biomedical Informatics
Bayes networks for estimating the number of solutions to a CSP
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Optimal Monte Carlo estimation of belief network inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Sample-and-accumulate algorithms for belief updating in Bayes networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Forecasting sleep apnea with dynamic network models
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
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A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless rho =NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks. Stochastic simulation methods, which often improve run times, provide an alternative to exact inference algorithms. Such a stochastic simulation algorithm, D-BNRAS, which is a randomized approximation scheme is presented. To analyze the run time, belief networks are parameterized, by the dependence value D/sub xi /, which is a measure of the cumulative strengths of the belief network dependencies given background evidence xi . This parameterization defines the class of f-dependence networks. The run time of D-BNRAS is polynomial when f is a polynomial function. Thus, the results prove the existence of a class of belief networks for which inference approximation is polynomial and, hence, provably faster than any exact algorithm.