High Precision Simulation Algorithm for Bayesian Network Inference
XU Hua-long · Jisuanji fangzhen · 2009
The current research status of inference methods for Bayesian Networks was reviewed, and a new inference algorithm based on Markov Chain Randomized Quasi-Monte Carlo (MCRQMC) was proposed. The new algorithm could provide high precision inference result and corresponding standard error at the same time. Comparative analysis of MCRQMC and currently used algorithms was conducted theoretically and experimentally. The experiments on the randomized halton sequence, sobol sequence and ordinary randomized sequence demonstrate that MCRQMC outperforms the conventional algorithms in terms of inference precision.