  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.
