Sequential Monte Carlo for Graphical Models

NeurIPS 2014 Christian A. NaessethFredrik LindstenThomas B. Schön

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces... (read more)

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