Pierre Pudlo

发表

Likelihood-Free Model Choice

Jean-Michel Marin, Arnaud Estoup, Pierre Pudlo, 2015, Handbook of Approximate Bayesian Computation.

Operator Norm Convergence of Spectral Clustering on Level Sets

Pierre Pudlo, Bruno Pelletier, Bruno Pelletier, 2010, J. Mach. Learn. Res..

Bayesian computation via empirical likelihood

Christian P Robert, Pierre Pudlo, Kerrie L Mengersen, 2012, Proceedings of the National Academy of Sciences.

Approximate Bayesian computational methods

Jean-Michel Marin, Christian P. Robert, Pierre Pudlo, 2011, Statistics and Computing.

Efficient learning in ABC algorithms

Jean-Marie Cornuet, Jean-Michel Marin, Pierre Pudlo, 2012, 1210.1388.

ABC model choice via random forests

Jean-Marie Cornuet, Jean-Michel Marin, Arnaud Estoup, 2014 .

Hidden Gibbs random fields model selection using Block Likelihood Information Criterion

Jean-Michel Marin, Pierre Pudlo, Julien Stoehr, 2016 .

DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data

Jean-Marie Cornuet, Arnaud Estoup, Pierre Pudlo, 2014, Bioinform..

ABC random forests for Bayesian parameter inference

Jean-Michel Marin, Christian P. Robert, Arnaud Estoup, 2019, Bioinform..

Elicitation of Experts' Knowledge for Functional Linear Regression

Pierre Pudlo, Paul-Marie Grollemund, Christophe Abraham, 2018 .

Approximate Bayesian computation via empirical likelihood

Christian P. Robert, Kerrie Mengersen, Pierre Pudlo, 2012 .

Combined parameter and state inference with automatically calibrated ABC.

Kerrie Mengersen, Pierre Pudlo, Paul Wu, 2019 .

Monte Carlo algorithms for model assessment via conflicting summaries

Sylvia Richardson, Pierre Pudlo, Christian Robert, 2011, 1106.5919.

Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"

Christophe Andrieu, Christian P. Robert, Mark Girolami, 2012 .

ABC model choice via random forests

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2014, 1406.6288.

Optimal parallelization of a sequential approximate Bayesian computation algorithm

Jean-Michel Marin, Pierre Pudlo, Mohammed Sedki, 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

Approximate Bayesian Computation via Random Forests

Pierre Pudlo, Jean-Michel Marin, 2015 .

Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields

Pierre Pudlo, Lionel Cucala, Julien Stoehr, 2014, Statistics and Computing.

Molecular Ecology Ressources – subject area: Methodological Advances 1 2 Estimation of demo-genetic model probabilities with Approximate Bayesian 3 Computation using linear discriminant analysis on summary statistics

Christian P Robert, Jean-Marie Cornuet, Jean-Michel Marin, 2012 .

Bayesian Functional Linear Regression with Sparse Step Functions

Meili Baragatti, Pierre Pudlo, Paul-Marie Grollemund, 2016, Bayesian Analysis.

The Normalized Graph Cut and Cheeger Constant: From Discrete to Continuous

Pierre Pudlo, Ery Arias-Castro, Bruno Pelletier, 2010, Advances in Applied Probability.

Estimation of density level sets with a given probability content

Pierre Pudlo, Bruno Pelletier, Bruno Pelletier, 2013 .

Consistency of adaptive importance sampling and recycling schemes

Jean-Michel Marin, Pierre Pudlo, Mohammed Sedki, 2019, Bernoulli.

Application of Approximate Bayesian Computation to infer the genetic history of Pygmy hunter-gatherers populations from Western Central Africa

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2018 .