[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2003.10.1.203

Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

Park, Ilsu (Department of Applied Mathematics, Yeosu National University)

Publication Information

Communications for Statistical Applications and Methods / v.10, no.1, 2003 , pp. 203-210 More about this Journal

Abstract

In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

Keywords

Hybrid Monte Carlo; Gibbs sampler; State-space model;

Citations & Related Records

Reference

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