Bayesian Estimation of State-Space Models Using Metropolis-Hastings Algorithm with Gibbs Sampling

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Journal Article
Computational Statistics and Data Analysis, 2001, 37 (2), pp. 151 - 170
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Abstract: In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state-space modeling in a Bayesian framework, which corresponds to an extension of Carlin et al. (J. Amer. Statist. Assoc. 87(418) (1992) 493â500) and Carter and Kohn (Biometrika 81(3) (1994) 541â553; Biometrika 83(3) (1996) 589â601). Using the Gibbs sampler and the MetropolisâHastings algorithm, an asymptotically exact estimate of the smoothing
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