  This paper introduces the factored conditional filter, a new filtering
algorithm for simultaneously tracking states and estimating parameters in
high-dimensional state spaces. The conditional nature of the algorithm is used
to estimate parameters and the factored nature is used to decompose the state
space into low-dimensional subspaces in such a way that filtering on these
subspaces gives distributions whose product is a good approximation to the
distribution on the entire state space. The conditions for successful
application of the algorithm are that observations be available at the subspace
level and that the transition model can be factored into local transition
models that are approximately confined to the subspaces; these conditions are
widely satisfied in computer science, engineering, and geophysical filtering
applications. We give experimental results on tracking epidemics and estimating
parameters in large contact networks that show the effectiveness of our
approach.

Artificial Intelligence;Machine Learning