HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

TL;DR

This paper introduces HUGS, a hybrid inference method combining exact computations and Gibbs sampling in junction trees to improve Bayesian network inference under resource constraints.

Contribution

It presents a novel hybrid inference scheme that extends junction tree message passing by integrating Gibbs sampling with exact inference methods.

Findings

Enhanced inference capabilities in Bayesian networks

Potential to solve previously intractable problems

Combines strengths of Monte Carlo and exact methods

Abstract

Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist exact solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of exact inference and Gibbs sampling in discrete Bayesian networks. We argue that this combination calls for an extension of the usual message passing scheme of ordinary junction trees.