Automatic Backward Filtering Forward Guiding for Markov processes and graphical models

7 Oct 2020 Frank van der Meulen Moritz Schauer

We incorporate discrete and continuous time Markov processes as building blocks into probabilistic graphical models with latent and observed variables. We introduce the automatic Backward Filtering Forward Guiding (BFFG) paradigm (Mider et al., 2020) for programmable inference on latent states and model parameters... (read more)

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  • 62H22, 60J25, 60J60, 60J80