A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
This work addresses a specific bottleneck in Bayesian network inference for researchers and practitioners, offering an incremental improvement over existing simulation methods.
The paper tackles the problem of probabilistic inference in Bayesian belief networks by introducing a new simulation scheme that generates more evenly spread samples than likelihood weighting, resulting in improved average runtime and error in belief estimates.
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.