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Optimal estimation and Cramer-Rao bounds for partial non-gaussian state space models
Linkoping Univ, Div Automat Control, S-58183 Linkoping, Sweden Univ Cambridge, Dept Engn, Signal Proc Grp, Cambridge CB2 1PZ, England Def Evaluat & Res Agcy, Malvern WR14 3PS, Worcs, England.
Linkoping Univ, Div Automat Control, S-58183 Linkoping, Sweden Univ Cambridge, Dept Engn, Signal Proc Grp, Cambridge CB2 1PZ, England Def Evaluat & Res Agcy, Malvern WR14 3PS, Worcs, England.
Linkoping Univ, Div Automat Control, S-58183 Linkoping, Sweden Univ Cambridge, Dept Engn, Signal Proc Grp, Cambridge CB2 1PZ, England Def Evaluat & Res Agcy, Malvern WR14 3PS, Worcs, England.
2001 (English)In: Annals of the Institute of Statistical Mathematics, ISSN 0020-3157, Vol. 53, no 1, 97-112 p.Article in journal (Refereed) Published
Abstract [en]

Partial non-Gaussian state-space models include many models of interest while keeping a convenient analytical structure. In this paper, two problems related to partial non-Gaussian models are addressed. First, we present an efficient sequential Monte Carlo method to perform Bayesian inference. Second, we derive simple recursions to compute posterior Cramer-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given.

Place, publisher, year, edition, pages
2001. Vol. 53, no 1, 97-112 p.
Keyword [en]
optimal estimation, Bayesian inference, sequential Monte Carlo methods, posterior Cramer-Rao bounds
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-49335OAI: oai:DiVA.org:liu-49335DiVA: diva2:270231
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2011-01-13

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