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math.ST2015★ 8 cited
Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Julien Stoehr, Nial Friel
Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoo…
math.ST2014★ 10 cited
Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
Julien Stoehr, Pierre Pudlo, Lionel Cucala
Selecting between different dependency structures of hidden Markov random field can be very challenging, due to the intractable normalizing constant in the likelihood. We answer th…