A Generalized Loop Correction Method for Approximate Inference in Graphical Models

TL;DR

This paper introduces Generalized Loop Correction (GLC), a novel inference method for graphical models that combines cavity distribution and region-based approaches, improving accuracy over existing methods.

Contribution

The paper proposes GLC, a new approximate inference algorithm that integrates cavity and region-based corrections, enhancing inference accuracy in loopy graphical models.

Findings

GLC outperforms existing loop correction methods in accuracy.

Empirical results show GLC's effectiveness across various models.

GLC effectively combines two correction strategies for better inference.

Abstract

Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but can be incorrect or non-convergent for loopy graphical models. Recently, several new approximate inference algorithms based on cavity distribution have been proposed. These methods can account for the effect of loops by incorporating the dependency between BP messages. Alternatively, region-based approximations (that lead to methods such as Generalized Belief Propagation) improve upon BP by considering interactions within small clusters of variables, thus taking small loops within these clusters into account. This paper introduces an approach, Generalized Loop Correction (GLC), that benefits from both of these types of loop correction. We show how GLC relates to these two families of inference methods, then provide empirical evidence that GLC works effectively in general, and can be significantly more accurate than both correction schemes.