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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.<\/jats:p>","DOI":"10.1111\/1467-9868.00363","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T19:37:40Z","timestamp":1047497860000},"page":"827-836","source":"Crossref","is-referenced-by-count":158,"title":["Particle Filtering for Partially Observed Gaussian State Space Models"],"prefix":"10.1093","volume":"64","author":[{"given":"Christophe","family":"Andrieu","sequence":"first","affiliation":[{"name":"University of Bristol , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnaud","family":"Doucet","sequence":"additional","affiliation":[{"name":"University of Cambridge , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2002,10,23]]},"reference":[{"key":"2023040109513427000_","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"J. 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