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• W1963294318 abstract "This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods. A theoretical result established recently by the authors shows that adapting interaction between particles to suitably control the effective sample size (ESS) is sufficient to guarantee stability of SMC algorithms. Our objective is to leverage this result and devise algorithms which are thus guaranteed to work well in a distributed setting. We make three main contributions to achieve this. First, we study mathematical properties of the ESS as a function of matrices and graphs that parameterize the interaction among particles. Secondly, we show how these graphs can be induced by tree data structures which model the logical network topology of an abstract distributed computing environment. Finally, we present efficient distributed algorithms that achieve the desired ESS control, perform resampling and operate on forests associated with these trees. © 2015 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2015" @default.
• W1963294318 created "2016-06-24" @default.
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• W1963294318 date "2015-07-14" @default.
• W1963294318 modified "2023-10-01" @default.
• W1963294318 title "Forest resampling for distributed sequential Monte Carlo" @default.
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• W1963294318 doi "https://doi.org/10.1002/sam.11280" @default.
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