Region Based Approximation for High Dimensional Bayesian Network Models

TL;DR

This paper introduces Triplet Region Construction (TRC), an approximate inference algorithm for high-dimensional Bayesian Networks that reduces clustering complexity from exponential to polynomial, ensuring convergence and accuracy.

Contribution

The paper proposes TRC, a novel approximate inference method that guarantees convergence and significantly reduces clustering complexity in large Bayesian Networks.

Findings

TRC reduces clustering complexity from exponential to polynomial.

TRC achieves accurate inference results compared to exact methods.

TRC guarantees convergence unlike MCMC algorithms.

Abstract

Performing efficient inference on Bayesian Networks (BNs), with large numbers of densely connected variables is challenging. With exact inference methods, such as the Junction Tree algorithm, clustering complexity can grow exponentially with the number of nodes and so computation becomes intractable. This paper presents a general purpose approximate inference algorithm called Triplet Region Construction (TRC) that reduces the clustering complexity for factorized models from worst case exponential to polynomial. We employ graph factorization to reduce connection complexity and produce clusters of limited size. Unlike MCMC algorithms TRC is guaranteed to converge and we present experiments that show that TRC achieves accurate results when compared with exact solutions.