Iterative Join-Graph Propagation

TL;DR

This paper introduces an iterative join-graph propagation algorithm that extends join-tree clustering to join-graphs, improving inference accuracy and efficiency in probabilistic graphical models.

Contribution

It proposes a novel iterative join-graph propagation method inspired by belief propagation and mini-clustering, enhancing approximate inference techniques.

Findings

Iterative join-graph propagation outperforms traditional IBP and mini-clustering in accuracy.

The method is often significantly more precise, sometimes by several orders of magnitude.

Empirical results show improved efficiency and accuracy across various problem classes.

Abstract

The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, iteratively. It is inspired by the success of Pearl's belief propagation algorithm as an iterative approximation scheme on one hand, and by a recently introduced mini-clustering i. success as an anytime approximation method, on the other. The proposed Iterative Join-graph Propagation IJGP belongs to the class of generalized belief propagation methods, recently proposed using analogy with algorithms in statistical physics. Empirical evaluation of this approach on a number of problem classes demonstrates that even the most time-efficient variant is almost always superior to IBP and MC i, and is sometimes more accurate by as much as several orders of magnitude.