Monolithic and Partial Compilation Methods for Probabilistic Inference of Bayesian Networks using ZBDDs.

Abstract

This paper addresses an algorithm for probabilistic inference for Bayesian Networks (BNs). Recently, we proposed the algorithm using Zero-suppressed BDDs for compiling BNs using "separation variables" which provides compact ZBDDs. In this paper, we provide the method for constructing ZBDDs only for related parts of networks. We show some experimental results to compare our new method with the previous one. The experimental results suggest that the new method is superior to the previous method when BNs have not many ancestors for each node, and when the number of instantiations are small.