Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Bayesian Estimation of State-Space Model Using the Hybrid Monte Carlo within Gibbs Sampler

  • Park, Ilsu (Department of Applied Mathematics, Yeosu National University)
  • Published : 2003.04.01
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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.

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References

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