A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

Simulation schemes for probabilistic infer­ ence in Bayesian belief networks offer many advantages over exact algorithms; for ex­ ample, these schemes have a linear and thus predictable runtime while exact algo­ rithms have exponential runtime. Exper­ iments have shown that likelihood weight­ ing is one of the most promising simulation schemes. In this paper, we present a new simulation scheme that generates samples more evenly spread in the sample space than the likelihood weighting scheme. We show both theoretically and experimentally that the stratified scheme outperforms likelihood weighting in average runtime and error in estimates of beliefs.