Optimal Monte Carlo Estimation of Belief Network Inference
  We present two Monte Carlo sampling algorithms for probabilistic inference
that guarantee polynomial-time convergence for a larger class of network than
current sampling algorithms provide. These new methods are variants of the
known likelihood weighting algorithm. We use of recent advances in the theory
of optimal stopping rules for Monte Carlo simulation to obtain an inference
approximation with relative error epsilon and a small failure probability
delta. We present an empirical evaluation of the algorithms which demonstrates
their improved performance.
