Markov Chains for Exploring Posterior Distributions

  title={Markov Chains for Exploring Posterior Distributions},
  author={Luke Tierney},
  journal={Annals of Statistics},
  • L. Tierney
  • Published 1994
  • Mathematics
  • Annals of Statistics
Several Markov chain methods are available for sampling from a posterior distribution. Two important examples are the Gibbs sampler and the Metropolis algorithm. In addition, several strategies are available for constructing hybrid algorithms. This paper outlines some of the basic methods and strategies and discusses some related theoretical and practical issues. On the theoretical side, results from the theory of general state space Markov chains can be used to obtain convergence rates, laws… Expand
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