Open Access
February 2013 Lookahead Strategies for Sequential Monte Carlo
Ming Lin, Rong Chen, Jun S. Liu
Statist. Sci. 28(1): 69-94 (February 2013). DOI: 10.1214/12-STS401


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.


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Ming Lin. Rong Chen. Jun S. Liu. "Lookahead Strategies for Sequential Monte Carlo." Statist. Sci. 28 (1) 69 - 94, February 2013.


Published: February 2013
First available in Project Euclid: 29 January 2013

zbMATH: 1332.62144
MathSciNet: MR3075339
Digital Object Identifier: 10.1214/12-STS401

Keywords: adaptive lookahead , lookahead sampling , lookahead weighting , multilevel , pilot lookahead , sequential Monte Carlo

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.28 • No. 1 • February 2013
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