Factored Particles for Scalable Monitoring

TL;DR

This paper introduces a new family of approximate algorithms for monitoring dynamic Bayesian networks, combining particle filtering and Boyen-Koller methods to improve scalability and accuracy.

Contribution

It presents factored particles, a novel approach that maintains belief states as sets of factored particles, enhancing performance over existing methods.

Findings

Outperforms traditional particle filtering

Outperforms Boyen-Koller algorithm

Effective on large systems

Abstract

Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms are necessary. This paper presents a new family of approximate monitoring algorithms that combine the best qualities of the particle filtering and Boyen-Koller methods. Our algorithms maintain an approximate representation the belief state in the form of sets of factored particles, that correspond to samples of clusters of state variables. Empirical results show that our algorithms outperform both ordinary particle filtering and the Boyen-Koller algorithm on large systems.