A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

TL;DR

This paper introduces a new stratified simulation scheme for Bayesian belief networks that improves sampling efficiency and accuracy over likelihood weighting, with better runtime and error performance demonstrated theoretically and experimentally.

Contribution

A novel stratified simulation scheme for Bayesian inference that outperforms likelihood weighting in efficiency and accuracy.

Findings

Stratified scheme generates more evenly spread samples.

Outperforms likelihood weighting in runtime and error.

Validated through theoretical analysis and experiments.

Abstract

Simulation schemes for probabilistic inference in Bayesian belief networks offer many advantages over exact algorithms; for example, these schemes have a linear and thus predictable runtime while exact algorithms have exponential runtime. Experiments have shown that likelihood weighting 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.