AISM 53, 97-112
© 2001 British Crown Copyright
(Received May 1, 2000; revised August 4, 2000)
Abstract. 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 Cramér-Rao bounds (PCRB). An application to jump Markov linear systems (JMLS) is given.
Key words and phrases: Optimal estimation, Bayesian inference, sequential Monte Carlo methods, posterior Cramér-Rao bounds.
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