Divide-and-conquer with sequential Monte Carlo

Article, Preprint English OPEN
Lindsten, F; Johansen, AM; Naesseth, CA; Kirkpatrick, B; Schön, TB; Aston, John; Bouchard-Côté, A;
(2014)

We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conquer approach based upon an auxiliary tree-structured decomposition of the model of interest... View more
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