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.
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Bergman, N., Doucet, A. & Gordon, N. Optimal Estimation and Cramér-Rao Bounds for Partial Non-Gaussian State Space Models. Annals of the Institute of Statistical Mathematics 53, 97–112 (2001). https://doi.org/10.1023/A:1017920621802
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DOI: https://doi.org/10.1023/A:1017920621802