688 citations · 1k across the 12 of their papers we have counts for
7 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…
Online data processing: comparison of Bayesian regularized particle filters
Roberto Casarin, Jean-Michel Marin
The aim of this paper is to compare three regularized particle filters in an online data processing context. We carry out the comparison in terms of hidden states filtering and par…
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…
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…