Advances in Applied Probability

Theory of segmented particle filters

Hock Peng Chan, Chiang-Wee Heng, and Ajay Jasra

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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.

Article information

Adv. in Appl. Probab., Volume 48, Number 1 (2016), 69-87.

First available in Project Euclid: 8 March 2016

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 64C05
Secondary: 62F15: Bayesian inference

CLT parallel processing SMC standard error estimation


Chan, Hock Peng; Heng, Chiang-Wee; Jasra, Ajay. Theory of segmented particle filters. Adv. in Appl. Probab. 48 (2016), no. 1, 69--87.

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