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This paper introduces the factored conditional filter, a new filteringalgorithm for simultaneously tracking states and estimating parameters inhigh-dimensional state spaces. The conditional nature of the algorithm is usedto estimate parameters and the factored nature is used to decompose the statespace into low-dimensional subspaces in such a way that filtering on thesesubspaces gives distributions whose product is a good approximation to thedistribution on the entire state space. The conditions for successfulapplication of the algorithm are that observations be available at the subspacelevel and that the transition model can be factored into local transitionmodels that are approximately confined to the subspaces; these conditions arewidely satisfied in computer science, engineering, and geophysical filteringapplications. We give experimental results on tracking epidemics and estimatingparameters in large contact networks that show the effectiveness of ourapproach.

Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces