Open Access
February 2014 Twisted particle filters
Nick Whiteley, Anthony Lee
Ann. Statist. 42(1): 115-141 (February 2014). DOI: 10.1214/13-AOS1167


We investigate sampling laws for particle algorithms and the influence of these laws on the efficiency of particle approximations of marginal likelihoods in hidden Markov models. Among a broad class of candidates we characterize the essentially unique family of particle system transition kernels which is optimal with respect to an asymptotic-in-time variance growth rate criterion. The sampling structure of the algorithm defined by these optimal transitions turns out to be only subtly different from standard algorithms and yet the fluctuation properties of the estimates it provides can be dramatically different. The structure of the optimal transition suggests a new class of algorithms, which we term “twisted” particle filters and which we validate with asymptotic analysis of a more traditional nature, in the regime where the number of particles tends to infinity.


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Nick Whiteley. Anthony Lee. "Twisted particle filters." Ann. Statist. 42 (1) 115 - 141, February 2014.


Published: February 2014
First available in Project Euclid: 18 February 2014

zbMATH: 1302.60139
MathSciNet: MR3178458
Digital Object Identifier: 10.1214/13-AOS1167

Primary: 60K35 , 62M20
Secondary: 60G35

Keywords: Filtering , sequential Monte Carlo

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.42 • No. 1 • February 2014
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