Jean-Michel Marin
University of Montpellier
139 Papers
1.8K Citations
Jean-Michel Marin is an academic researcher from University of Montpellier. The author has contributed to research in topics: Bayesian inference & Approximate Bayesian computation. The author has an hindex of 37, co-authored 138 publications. Previous affiliations of Jean-Michel Marin include University of Miami & Paris Dauphine University.
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Papers
Joint inference of adaptive and demographic history from temporal population genomic data
TL;DR: In this article, a framework based on ABC via Random Forests is proposed to jointly estimate demographic and selection parameters from temporal population genomic data (e.g., experimental evolution, monitored populations, ancient DNA).
Bayesian Inference and Computation
Christian P. Robert,Jean-Michel Marin,Judith Rousseau +2 more
- 12 Sep 2011
TL;DR: This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal method for summarising uncertainty and making estimates and predictions using probability statements conditional on observed data and an assumed model.
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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,Simon Barthelmé,Nicolas Chopin,Julien Cornebise,Arnaud Doucet,Mark Girolami,Ioannis Kosmidis,Ajay Jasra,Anthony Lee,Jean-Michel Marin,Pierre Pudlo,Christian P. Robert,Mohammed Sedki,Sumeetpal S. Singh +13 more
TL;DR: This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply from the authors.
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Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig
Christian P. Robert,Gilles Celeux,Jack Jewson,Julie Josse,Jean-Michel Marin,Christian P. Robert +5 more
TL;DR: This note is a collection of several discussions of the paper "Beyond subjective and objective in statistics", read by A. Gelman and C. Hennig to the Royal Statistical Society on April 12, 2017, and to appear in the Journal of the Royal statistical Society, Series A.
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On computational tools for Bayesian data analysis
TL;DR: While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures.
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