Theory of segmented particle filters

Hock Peng Chan, Chiang Wee Heng, Ajay Jasra

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.
Original languageEnglish (US)
JournalAdvances in Applied Probability
Issue number1
StatePublished - Mar 1 2016
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2019-11-20


Dive into the research topics of 'Theory of segmented particle filters'. Together they form a unique fingerprint.

Cite this