Factored Particles for Scalable Monitoring
  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.
