2013/03/27 by Homer L. Chin, Chin, Homer L., Gregory F. Cooper +1
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.2722
Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper examines Bayesian belief network inference using simulation as a method for computing the posterior probabilities of network variables. Specifically, it examines the use of a method described by Henrion, called logic sampling, and a method described by Pearl, called stochastic simulation. We first review the conditions under which logic sampling is computationally infeasible. Such cases motivated the development of the Pearl's stochastic simulation algorithm. We have found that this stochastic simulation algorithm, when applied to certain networks, leads to much slower than expected convergence to the true posterior probabilities. This behavior is a result of the tendency for local areas in the network to become fixed through many simulation cycles. The time required to obtain significant convergence can be made arbitrarily long by strengthening the probabilistic dependency between nodes. We propose the use of several forms of graph modification, such as graph pruning, arc reversal, and node reduction, in order to convert some networks into formats that are computationally more efficient for simulation.