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March 2016 Theory of segmented particle filters
Hock Peng Chan, Chiang-Wee Heng, Ajay Jasra
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Adv. in Appl. Probab. 48(1): 69-87 (March 2016).

Abstract

We study the asymptotic behavior of a new particle filter approach for the estimation of hidden Markov models. In particular, we develop an algorithm where the latent-state sequence is segmented into multiple shorter portions, with an estimation technique based upon a separate particle filter in each portion. The partitioning facilitates the use of parallel processing, which reduces the wall-clock computational time. Based upon this approach, we introduce new estimators of the latent states and likelihood which have similar or better variance properties compared to estimators derived from standard particle filters. We show that the likelihood function estimator is unbiased, and show asymptotic normality of the underlying estimators.

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Hock Peng Chan. Chiang-Wee Heng. Ajay Jasra. "Theory of segmented particle filters." Adv. in Appl. Probab. 48 (1) 69 - 87, March 2016.

Information

Published: March 2016
First available in Project Euclid: 8 March 2016

zbMATH: 1338.65023
MathSciNet: MR3473568

Subjects:
Primary: 64C05
Secondary: 62F15

Rights: Copyright © 2016 Applied Probability Trust

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Vol.48 • No. 1 • March 2016
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