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
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
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
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Journal of Artificial Intelligence Research
Probabilistic partial evaluation: exploiting rule structure in probabilistic inference
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Adaptive importance sampling for estimation in structured domains
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Localized partial evaluation of belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Some properties of joint probability distributions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Backward simulation in Bayesian networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Knowledge engineering for large belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Incremental probabilistic inference
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
The use of conflicts in searching Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
An importance sampling algorithm based on evidence pre-propagation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Theoretical analysis and practical insights on importance sampling in Bayesian networks
International Journal of Approximate Reasoning
Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Approximate counting by sampling the backtrack-free search space
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generalized evidence pre-propagated importance sampling for hybrid Bayesian networks
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Approximate inference in probabilistic graphical models with determinism
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
International Journal of Approximate Reasoning
SampleSearch: Importance sampling in presence of determinism
Artificial Intelligence
Importance sampling on Bayesian networks with deterministic causalities
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Importance sampling-based estimation over AND/OR search spaces for graphical models
Artificial Intelligence
Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
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Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in the face of extremely unlikely evidence. In addressing this problem, importance sampling algorithms seem to be most successful. We discuss the principles underlying the importance sampling algorithms in Bayesian networks. After that, we describe Evidence Pre-propagation Importance Sampling (EPIS-BN), an importance sampling algorithm that computes an importance function using two techniques: loopy belief propagation [K. Murphy, Y. Weiss, M. Jordan, Loopy belief propagation for approximate inference: An empirical study, in: Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence, UAI-99, San Francisco, CA, Morgan Kaufmann Publishers, 1999, pp. 467-475; Y. Weiss, Correctness of local probability propagation in graphical models with loops, Neural Computation 12 (1) (2000) 1-41] and @e-cutoff heuristic [J. Cheng, M.J. Druzdzel, BN-AIS: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks, Journal of Artificial Intelligence Research 13 (2000) 155-188]. We tested the performance of EPIS-BN on three large real Bayesian networks and observed that on all three networks it outperforms AIS-BN [J. Cheng, M.J. Druzdzel, BN-AIS: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks, Journal of Artificial Intelligence Research 13 (2000) 155-188], the current state-of-the-art algorithm, while avoiding its costly learning stage. We also compared EPIS-BN Gibbs sampling and discuss the role of the @e-cutoff heuristic in importance sampling for Bayesian networks. networks.