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Exact Bayesian inference on state-space models~(SSM) is in generaluntractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods donot yield correct approximations for complex models. In this paper, we proposea mixed inference algorithm that computes closed-form solutions using beliefpropagation as much as possible, and falls back to sampling-based SMC methodswhen exact computations fail. This algorithm thus implements automaticRao-Blackwellization and is even exact for Gaussian tree models.

Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation