Value Elimination: Bayesian Inference via Backtracking Search

TL;DR

This paper explores using backtracking search for Bayesian inference, demonstrating it can outperform traditional methods by leveraging context-specific structures and providing competitive performance guarantees.

Contribution

It introduces a novel application of backtracking search to Bayesian inference, showing potential for improved performance over existing algorithms.

Findings

Backtracking search can match performance guarantees of standard Bayesian algorithms.

It can outperform traditional methods on certain problem instances.

A new Bayesian inference engine using backtracking is developed and shown to be competitive.

Abstract

Backtracking search is a powerful algorithmic paradigm that can be used to solve many problems. It is in a certain sense the dual of variable elimination; but on many problems, e.g., SAT, it is vastly superior to variable elimination in practice. Motivated by this we investigate the application of backtracking search to the problem of Bayesian inference (Bayes). We show that natural generalizations of known techniques allow backtracking search to achieve performance guarantees similar to standard algorithms for Bayes, and that there exist problems on which backtracking can in fact do much better. We also demonstrate that these ideas can be applied to implement a Bayesian inference engine whose performance is competitive with standard algorithms. Since backtracking search can very naturally take advantage of context specific structure, the potential exists for performance superior to standard algorithms on many problems.