Join-Graph Propagation Algorithms

Abstract

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.

References in corpus (7)

• Expectation Propagation for approximate Bayesian inference
• Survey propagation: an algorithm for satisfiability
• Loop series for discrete statistical models on graphs
• An Importance Sampling Algorithm Based on Evidence Pre-propagation
• A Scheme for Approximating Probabilistic Inference
• Value Elimination: Bayesian Inference via Backtracking Search
• Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks