Lookahead Strategies for Sequential Monte Carlo

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Lookahead Strategies for Sequential Monte Carlo

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Title: Lookahead Strategies for Sequential Monte Carlo
Author: Lin, Ming; Chen, Rong; Liu, Jun

Note: Order does not necessarily reflect citation order of authors.

Citation: Lin, Ming, Rong Chen, and Jun S. Liu. 2013. “Lookahead Strategies for Sequential Monte Carlo.” Statistical Science 28 (1) (February): 69–94. doi:10.1214/12-sts401.
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Abstract: Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the “future” information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.
Published Version: doi:10.1214/12-STS401
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:14169383
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