834 citations · 3.9k across the 95 of their papers we have counts for
46 papers · 1 filter
Insufficient Gibbs Sampling
Antoine Luciano, Christian P. Robert, Robin J. Ryder
In some applied scenarios, the availability of complete data is restricted, often due to privacy concerns; only aggregated, robust and inefficient statistics derived from the data…
50 shades of Bayesian testing of hypotheses
Christian P Robert
Hypothesis testing and model choice are quintessential questions for statistical inference and while the Bayesian paradigm seems ideally suited for answering these questions, it fa…
Approximating Bayes in the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statist…
Living on the Edge: An Unified Approach to Antithetic Sampling
Roberto Casarin, Radu V. Craiu, Lorenzo Frattarolo +1
We identify recurrent ingredients in the antithetic sampling literature leading to a unified sampling framework. We introduce a new class of antithetic schemes that includes the mo…
Generalized Poisson Difference Autoregressive Processes
Giulia Carallo, Roberto Casarin, Christian P. Robert
This paper introduces a new stochastic process with values in the set Z of integers with sign. The increments of process are Poisson differences and the dynamics has an autoregress…
Approximate Bayesian computation with the Wasserstein distance
Espen Bernton, Pierre E. Jacob, Mathieu Gerber +1
A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation (ABC) has become a popular…
Model Selection for Mixture Models - Perspectives and Strategies
Gilles Celeux, Sylvia Fruewirth-Schnatter, Christian P. Robert
Determining the number G of components in a finite mixture distribution is an important and difficult inference issue. This is a most important question, because statistical infere…
Abandon Statistical Significance
Blakeley B. McShane, David Gal, Andrew Gelman +2
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these probl…
Better together? Statistical learning in models made of modules
Pierre E. Jacob, Lawrence M. Murray, Chris C. Holmes +1
In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference, prediction, or decision problem. In such circumstances, it…
Jeffreys priors for mixture estimation: properties and alternatives
Clara Grazian, Christian P. Robert
While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they are not available in closed form. Furthermore, they often are improper priors.…
Some discussions on the Read Paper "Beyond subjective and objective in statistics" by A. Gelman and C. Hennig
Gilles Celeux, Jack Jewson, Julie Josse +2
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 A…
On parameter estimation with the Wasserstein distance
Espen Bernton, Pierre E. Jacob, Mathieu Gerber +1
Statistical inference can be performed by minimizing, over the parameter space, the Wasserstein distance between model distributions and the empirical distribution of the data. We…
Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer
Christian P. Robert, Judith Rousseau
These are written comments about the Read Paper A Bayesian criterion for singular models by M. Drton and M. Plummer, read to the Royal Statistical Society on October 5, 2016. The d…
Some comments about "Penalising model component complexity" by Simpson et al. (2017)
Christian P. Robert, Judith Rousseau
This note discusses the paper "Penalising model component complexity" by Simpson et al. (2017). While we acknowledge the highly novel approach to prior construction and commend the…
ABC random forests for Bayesian parameter inference
Louis Raynal, Jean-Michel Marin, Pierre Pudlo +3
This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). Approximate Bayesian computation (ABC) h…
Some comments about James Watson's and Chris Holmes' "Approximate Models and Robust Decisions": Nonparametric Bayesian clay for robust decision bricks
Christian P. Robert, Judith Rousseau
This note discusses Watson and Holmes (2016) and their pro- posals towards more robust Bayesian decisions. While we acknowledge and commend the authors for setting new and all-enco…
Weakly informative reparameterisations for location-scale mixtures
Kaniav Kamary, Jeong Eun Lee, Christian P. Robert
While mixtures of Gaussian distributions have been studied for more than a century (Pearson, 1894), the construction of a reference Bayesian analysis of those models still remains…
Jeffreys priors for mixture estimation
Clara Grazian, Christian Robert
While Jeffreys priors usually are well-defined for the parameters of mixtures of distributions, they are not available in closed form. Furthermore, they often are improper priors.…
The expected demise of the Bayes factor
Christian P. Robert
This note is a discussion commenting on the paper by Ly et al. on "Harold Jeffreys's Default Bayes Factor Hypothesis Tests: Explanation, Extension, and Application in Psychology" a…
Bayesian Essentials with R: The Complete Solution Manual
Christian P. Robert, Jean-Michel Marin
This is the collection of solutions for all the exercises proposed in Bayesian Essentials with R (2014).
Likelihood-free Model Choice
Jean-Michel Marin, Pierre Pudlo, Arnaud Estoup +1
This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing t…
A discussion of "Bayesian model selection based on proper scoring rules" by A.P. Dawid and M. Musio
Clara Grazian, Ilaria Masiani, Christian P. Robert
This note is a discussion of the article "Bayesian model selection based on proper scoring rules" by A.P. Dawid and M. Musio, to appear in Bayesian Analysis. While appreciating the…
Some comments about A. Ronald Gallant's "Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference"
Christian P. Robert
This note is commenting on Ronald Gallant's (2015) reflections on the construction of Bayesian prior distributions from moment conditions. The main conclusion is that the paper doe…
Testing hypotheses via a mixture estimation model
Kaniav Kamary, Kerrie Mengersen, Christian P. Robert +1
We consider a novel paradigm for Bayesian testing of hypotheses and Bayesian model comparison. Our alternative to the traditional construction of posterior probabilities that a giv…