  Computing the exact likelihood of data in large Bayesian networks consisting
of thousands of vertices is often a difficult task. When these models contain
many deterministic conditional probability tables and when the observed values
are extremely unlikely even alternative algorithms such as variational methods
and stochastic sampling often perform poorly. We present a new importance
sampling algorithm for Bayesian networks which is based on variational
techniques. We use the updates of the importance function to predict whether
the stochastic sampling converged above or below the true likelihood, and
change the proposal distribution accordingly. The validity of the method and
its contribution to convergence is demonstrated on hard networks of large
genetic linkage analysis tasks.

Computation;Artificial Intelligence