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Poster
in
Workshop: Structured Probabilistic Inference and Generative Modeling

Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation

Waïss Azizian · Guillaume Baudart · Marc Lelarge

Keywords: [ Graphical Models ] [ Probabilistic Inference ] [ Kalman filter ] [ reactive programming ]


Abstract:

Exact Bayesian inference on state-space models (SSM) is in general untractable and, unfortunately, basic Sequential Monte Carlo (SMC) methods do not yield correct approximations for complex models. In this paper, we propose a mixed inference algorithm that computes closed-form solutions using Belief Propagation as much as possible, and falls back to sampling-based SMC methods when exact computations fail. This algorithm thus implements automatic Rao-Blackwellization and is even exact for Gaussian tree models.

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