Bayesian estimation of state-space models using the MetropolisHastings algorithm within Gibbs sampling
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) 493500) and Carter and Kohn (Biometrika 81(3) (1994) 541553; Biometrika 83(3) (1996) 589601). Using the Gibbs sampler and the MetropolisHastings algorithm, an asymptotically exact estimate of the smoothing mean is obtained from any nonlinear and/or non-Gaussian model. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed Bayes estimator.
bayesian estimation state-space model metropolishastings algorithm gibbs sampler proposal density markov chain monte carlo nonlinear and/or non-gaussian smoothing