834 citations · 3.9k across the 95 of their papers we have counts for
7 papers · 2 filters
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…
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…
On variance stabilisation by double Rao-Blackwellisation
Alessandra Iacobucci, Jean-Michel Marin, Christian Robert
Population Monte Carlo has been introduced as a sequential importance sampling technique to overcome poor fit of the importance function. In this paper, we compare the performances…
A Bayesian reassessment of nearest-neighbour classification
Lionel Cucala, Jean-Michel Marin, Christian Robert +1
The k-nearest-neighbour procedure is a well-known deterministic method used in supervised classification. This paper proposes a reassessment of this approach as a statistical techn…
Properties of Nested Sampling
Nicolas Chopin, Christian Robert
Nested sampling is a simulation method for approximating marginal likelihoods proposed by Skilling (2006). We establish that nested sampling has an approximation error that vanishe…