An Empirical Evaluation of Possible Variations of Lazy Propagation

TL;DR

This paper empirically evaluates how different message computation algorithms, including VE, SPI, and AR, affect the performance of Lazy propagation in Bayesian networks, finding AR often yields the best results.

Contribution

It introduces an empirical comparison of message computation algorithms for Lazy propagation, highlighting the impact of AR in certain network cases.

Findings

Performance largely independent of message algorithm in most networks

AR outperforms VE and SPI in some randomly generated networks

Choice of message algorithm affects space and time efficiency in specific cases

Abstract

As real-world Bayesian networks continue to grow larger and more complex, it is important to investigate the possibilities for improving the performance of existing algorithms of probabilistic inference. Motivated by examples, we investigate the dependency of the performance of Lazy propagation on the message computation algorithm. We show how Symbolic Probabilistic Inference (SPI) and Arc-Reversal (AR) can be used for computation of clique to clique messages in the addition to the traditional use of Variable Elimination (VE). In addition, the paper resents the results of an empirical evaluation of the performance of Lazy propagation using VE, SPI, and AR as the message computation algorithm. The results of the empirical evaluation show that for most networks, the performance of inference did not depend on the choice of message computation algorithm, but for some randomly generated networks the choice had an impact on both space and time performance. In the cases where the choice had an impact, AR produced the best results.