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
Approximating probabilistic inference in Bayesian belief networks is NP-hard
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
An optimal approximation algorithm for Bayesian inference
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
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
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
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In this paper we introduce an improvement over importance sampling propagation algorithms in Bayesian networks. The difference with respect to importance sampling is that during the simulation, configurations are obtained using antithetic variables (variables with negative correlation), achieving a reduction of the variance of the estimation. The performance of the new algorithm is tested by means of some experiments carried out over four large real-world networks.