Open Access
June 2011 Iterated filtering
Edward L. Ionides, Anindya Bhadra, Yves Atchadé, Aaron King
Ann. Statist. 39(3): 1776-1802 (June 2011). DOI: 10.1214/11-AOS886


Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right.


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Edward L. Ionides. Anindya Bhadra. Yves Atchadé. Aaron King. "Iterated filtering." Ann. Statist. 39 (3) 1776 - 1802, June 2011.


Published: June 2011
First available in Project Euclid: 25 July 2011

zbMATH: 1220.62103
MathSciNet: MR2850220
Digital Object Identifier: 10.1214/11-AOS886

Primary: 62M09

Keywords: Dynamic systems , Filtering , importance sampling , partially observed Markov process , sequential Monte Carlo , state space model

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.39 • No. 3 • June 2011
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