The use of conflicts in searching Bayesian networks

TL;DR

This paper introduces a novel search-based algorithm for Bayesian networks that uses conflicts from diagnosis to efficiently estimate probabilities, especially in cases with extreme probabilities, demonstrated on large networks.

Contribution

It adapts conflicts from diagnosis to Bayesian inference, creating an anytime algorithm that improves efficiency over naive methods, particularly for large networks with extreme probabilities.

Findings

Effective in large networks with tens of thousands of nodes

Provides error bounds at any computation stage

Outperforms naive algorithms in efficiency

Abstract

This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorithm is most suited to the case with extreme probabilities. This paper presents a solution to the inefficiencies found in naive algorithms, and shows how the tools of the consistency-based diagnosis community (namely conflicts) can be used effectively to improve the efficiency. Empirical results with networks having tens of thousands of nodes are presented.