Statistical Science

Lookahead Strategies for Sequential Monte Carlo

Ming Lin, Rong Chen, and Jun S. Liu

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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.

Article information

Source
Statist. Sci., Volume 28, Number 1 (2013), 69-94.

Dates
First available in Project Euclid: 29 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.ss/1359468409

Digital Object Identifier
doi:10.1214/12-STS401

Mathematical Reviews number (MathSciNet)
MR3075339

Zentralblatt MATH identifier
1332.62144

Keywords
Sequential Monte Carlo lookahead weighting lookahead sampling pilot lookahead multilevel adaptive lookahead

Citation

Lin, Ming; Chen, Rong; Liu, Jun S. Lookahead Strategies for Sequential Monte Carlo. Statist. Sci. 28 (2013), no. 1, 69--94. doi:10.1214/12-STS401. https://projecteuclid.org/euclid.ss/1359468409


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