By Geweke J., Tanizaki H.
During this paper, an try out is made to teach a basic option 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). utilizing the Gibbs sampler and the Metropolis-Hastings set of rules, an asymptotically detailed estimate of the smoothing suggest is acquired from any nonlinear and/or non-Gaussian version. furthermore, taking a number of applicants of the thought density functionality, we research precision of the proposed Bayes estimator.
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