Statistics and Computing

, Volume 12, Issue 1, pp 27-36

First online:

On Bayesian model and variable selection using MCMC

  • Petros Dellaportas
  • , Jonathan J. Forster
  • , Ioannis Ntzoufras

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Several MCMC methods have been proposed for estimating probabilities of models and associated 'model-averaged' posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.

Gibbs sampler independence sampler Metropolis–Hastings reversible jump