Particle filtering for partially observed Gaussian state space models

C Andrieu, A Doucet

Research output: Contribution to journalArticle (Academic Journal)peer-review

141 Citations (Scopus)


Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models. This algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms. It differs from previous algorithms which were only applicable to conditionally linear Gaussian state space models. Computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models.
Translated title of the contributionParticle filtering for partially observed Gaussian state space models
Original languageEnglish
Pages (from-to)827 - 836
Number of pages10
JournalJournal of the Royal Statistical Society: Series B, Statistical Methodology
Volume64 (4)
Publication statusPublished - Oct 2002

Bibliographical note

Publisher: Blackwell
Other identifier: IDS Number: 614ZJ


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