Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Stable local computation with conditional Gaussian distributions
Statistics and Computing
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
Exact Inference in Networks with Discrete Children of Continuous Parents
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Journal of Artificial Intelligence Research
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth 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
Nonlinear deterministic relationships in bayesian networks
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Importance sampling algorithms for Bayesian networks: Principles and performance
Mathematical and Computer Modelling: An International Journal
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In this paper, we first provide a new theoretical understanding of the Evidence Pre-propagated Importance Sampling algorithm (EPIS-BN) (Yuan & Druzdzel 2003; 2006b) and show that its importance function minimizes the KL-divergence between the function itself and the exact posterior probability distribution in Polytrees. We then generalize the method to deal with inference in general hybrid Bayesian networks consisting of deterministic equations and arbitrary probability distributions. Using a novel technique called soft arc reversal, the new algorithm can also handle evidential reasoning with observed deterministic variables.