ABC likelihood-free methods for model choice in Gibbs random fields

  title={ABC likelihood-free methods for model choice in Gibbs random fields},
  author={Aude Grelaud},
Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods is hampered by the unavailability of the normalising constant in the Gibbs likelihood. In particular, from a Bayesian perspective, the computation of the posterior probabilities of the… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 21 references

An efficient MCMC algorithm method for distributions with intractable normalising constant

J. Møller, A. Pettitt, R. Reeves, K. Berthelsen.
Biometrika 93:451– 458. • 2006
View 3 Excerpts
Highly Influenced

Gaussian Random Fields: Theory and Applications

H. Rue, L. Held.
Chapman and Hall/CRC, Boca Raton, FL. • 2005
View 1 Excerpt

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