2013/03/27 by R. Martin Chavez, Chavez, R. Martin, Gregory F. Cooper +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Machine Learning and Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.1097
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of exact probabilistic inference on belief networks almost certainly requires exponential computation in the worst ease [3]. We have previously described a randomized approximation scheme, called BN-RAS, for computation on belief networks [ 1, 2, 4]. We gave precise analytic bounds on the convergence of BN-RAS and showed how to trade running time for accuracy in the evaluation of posterior marginal probabilities. We now extend our previous results and demonstrate the generality of our framework by applying similar mathematical techniques to the analysis of convergence for logic sampling [7], an alternative simulation algorithm for probabilistic inference.