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The Generalized Gibbs Sampler and the Neighborhood Sampler

In: Monte Carlo and Quasi-Monte Carlo Methods 2006

Author

Listed:
  • Jonathan Keith

    (Queensland University of Technology, School of Mathematical Sciences
    University of Queensland, Department of Mathematics)

  • George Sofronov

    (University of Queensland, Department of Mathematics)

  • Dirk Kroese

    (University of Queensland, Department of Mathematics)

Abstract

The Generalized Gibbs Sampler (GGS) is a recently proposed Markov chain Monte Carlo (MCMC) technique that is particularly useful for sampling from distributions defined on spaces in which the dimension varies from point to point or in which points are not easily defined in terms of co-ordinates. Such spaces arise in problems involving model selection and model averaging and in a number of interesting problems in computational biology. Such problems have hitherto been the domain of the Reversible-jump Sampler, but the method described here, which generalizes the well-known conventional Gibbs Sampler, provides an alternative that is easy to implement and often highly efficient.

Suggested Citation

  • Jonathan Keith & George Sofronov & Dirk Kroese, 2008. "The Generalized Gibbs Sampler and the Neighborhood Sampler," Springer Books, in: Alexander Keller & Stefan Heinrich & Harald Niederreiter (ed.), Monte Carlo and Quasi-Monte Carlo Methods 2006, pages 537-547, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-74496-2_31
    DOI: 10.1007/978-3-540-74496-2_31
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