834 citations · 3.9k across the 95 of their papers we have counts for
10 papers · 1 filter
ABC likelihood-freee methods for model choice in Gibbs random fields
Aude Grelaud, Christian Robert, Jean-Michel Marin +2
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…
Adaptive approximate Bayesian computation
Mark A. Beaumont, Jean-Marie Cornuet, Jean-Michel Marin +1
Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this metho…
Bayesian computation for statistical models with intractable normalizing constants
Yves Atchade, Nicolas Lartillot, Christian P. Robert
This paper deals with some computational aspects in the Bayesian analysis of statistical models with intractable normalizing constants. In the presence of intractable normalizing c…
Approximating the marginal likelihood in mixture models
J. -M. Marin, Christian Robert
In Chib (1995), a method for approximating marginal densities in a Bayesian setting is proposed, with one proeminent application being the estimation of the number of components in…
Bayesian Inference on Mixtures of Distributions
Kate Lee, Jean-Michel Marin, Kerrie Mengersen +1
This survey covers state-of-the-art Bayesian techniques for the estimation of mixtures. It complements the earlier Marin, Mengersen and Robert (2005) by studying new types of distr…
Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
Jean-Marie Cornuet, Filipe Santos, Mark A. Beaumont +5
Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract this information (at least partially) but they requir…