  We consider filtering for a continuous-time, or asynchronous, stochastic
system where the full distribution over states is too large to be stored or
calculated. We assume that the rate matrix of the system can be compactly
represented and that the belief distribution is to be approximated as a product
of marginals. The essential computation is the matrix exponential. We look at
two different methods for its computation: ODE integration and uniformization
of the Taylor expansion. For both we consider approximations in which only a
factored belief state is maintained. For factored uniformization we demonstrate
that the KL-divergence of the filtering is bounded. Our experimental results
confirm our factored uniformization performs better than previously suggested
uniformization methods and the mean field algorithm.
