Recursive Bayesian estimation using piece-wise constant approximations
Automatica (Journal of IFAC)
A comparison of three non-linear filters
Automatica (Journal of IFAC)
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Bayesian inference for stochastic time-varying copula models
Computational Statistics & Data Analysis
A Bayesian approach to term structure modeling using heavy-tailed distributions
Applied Stochastic Models in Business and Industry
An efficient sampling scheme for dynamic generalized models
Computational Statistics
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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) 493-500) and Carter and Kohn (Biometrika 81(3) (1994) 541-553; Biometrika 83(3) (1996) 589-601). Using the Gibbs sampler and the Metropolis-Hastings 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.