Abstract
Sampling from belief networks which have a substantial number of zero probabilities is problematic. MCMC algorithms like Gibbs sampling do not converge and importance sampling schemes generate many zero weight samples that are rejected, yielding an inefficient sampling process (the rejection problem). In this paper, we propose to augment importance sampling with systematic constraint-satisfaction search in order to overcome the rejection problem. The resulting SampleSearch scheme can be made unbiased by using a computationally expensive weighting scheme. To overcome this an approximation is proposed such that the resulting estimator is asymptotically unbiased. Our empirical results demonstrate the potential of our new scheme.