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

Abstract

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) 493â¿¿500) and Carter and Kohn (Biometrika 81(3) (1994) 541â¿¿553; Biometrika 83(3) (1996) 589â¿¿601). Using the Gibbs sampler and the Metropolisâ¿¿Hastings algorithm, an asymptotically exact estimate of the smoothing