Factored Particles for Scalable Monitoring
Brenda Ng, Leonid Peshkin, Avi Pfeffer
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.
PDF Link: /papers/02/p370-ng.pdf
AUTHOR = "Brenda Ng
and Leonid Peshkin and Avi Pfeffer",
TITLE = "Factored Particles for Scalable Monitoring",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "370--377"