Pseudo-Perfect and Adaptive Variants of the Metropolis-Hastings Algorithm with an Independent Candidate Density

orange ball Authors:
  J.N. Corcoran and U. Schneider

orange ball Date:
  January 2004

orange ball Status:
  Journal of Statistical Computation and Simulation, 2005, 75 (6), 459-475.

orange ball Abstract:

We describe and examine an imperfect variant of a perfect sampling algorithm based on the Metropolis-Hastings algorithm that appears to perform better than a more traditional approach in terms of speed and accuracy. We then describe and examine an "adaptive" Metropolis-Hasting algorithm which generates and updates a self-target candidate density in such a way that there is no "wrong choice" for an initial candidate density. Simulation examples are provided.

orange ball Link to paper: (J. Stat. Comp. Sim.)
  Click here.

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