C. Robert

发表

Rao-Blackwellisation of sampling schemes

G. Casella, C. Robert, 1996 .

Perfect Slice Samplers for Mixtures of Distributions

G. Casella, C. Robert, K. Mengersen, 1999 .

Superintelligence: Paths, Dangers, Strategies

C. Robert, 2017 .

Explaining the Perfect Sampler

G. Casella, C. Robert, M. Lavine, 2001 .

Optimal Sample Size for Multiple Testing : the Case of Gene Expression Mi roarraysPeter

C. Robert, G. Parmigiani, J. Rousseau, 2004 .

The Bayesian choice : a decision-theoretic motivation

C. Robert, 1996 .

Computational and Inferential Difficulties with Mixture Posterior Distributions

C. Robert, G. Celeux, M. Hurn, 2000 .

Post-Processing Accept-Reject Samples: Recycling and Rescaling

G. Casella, C. Robert, 1998 .

Bayesian computational methods

Christian P. Robert, C. Robert, 2010, 1002.2702.

Estimation of Finite Mixture Distributions Through Bayesian Sampling

C. Robert, J. Diebolt, 1994 .

Inference in generative models using the Wasserstein distance

C. Robert, Espen Bernton, P. Jacob, 2017, 1701.05146.

Monte Carlo Statistical Methods (Springer Texts in Statistics)

Christian P. Robert, George Casella, G. Casella, 2005 .

Discretization and Mcmc Convergence Assessment

C. Robert, 1998 .

Monte Carlo Statistical Methods

Christian P. Robert, George Casella, G. Casella, 2005, Springer Texts in Statistics.

Expectation Propagation as a Way of Life

J. Cunningham, C. Robert, A. Gelman, 2020 .

Convergence Control Methods for Markov Chain Monte Carlo Algorithms

C. Robert, 1995 .

Convergence Controls for MCMC Algorithms with Applications to Hidden Markov Chains

C. Robert, D. M. Titterington, T. Rydén, 1999 .

Discussion: Markov Chains for Exploring Posterior Distributions

C. Robert, 1994 .

The Bayesian choice : from decision-theoretic foundations to computational implementation

Christian P. Robert, C. Robert, 2007 .

Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers

O. Cappé, C. Robert, T. Rydén, 2003 .

Markov Chain Monte Carlo: 10 Years and Still Running!

O. Cappé, C. Robert, 2000 .

Mixtures of Distributions: Inference and Estimation

C. Robert, 1996 .

Introducing Monte Carlo Methods with R (Use R)

Christian P. Robert, George Casella, G. Casella, 2009 .

Controlled MCMC for Optimal Sampling

C. Robert, C. Andrieu, 2001 .

Computational methods for Bayesian model choice

C. Robert, D. Wraith, 2009, 0907.5123.

Big Bayes Stories—Foreword

C. Robert, K. Mengersen, 2014 .

Sure independence screening for ultrahigh dimensional feature space Discussion

Jeffrey S. Morris, Gareth M. James, Hao Helen Zhang, 2008 .

Optimal Sample Size for Multiple Testing

P. Müller, C. Robert, G. Parmigiani, 2004 .

Asymptotic properties of approximate Bayesian computation

David T. Frazier, C. Robert, J. Rousseau, 2016, Biometrika.

Importance sampling methods for Bayesian discrimination between embedded models

C. Robert, J. Marin, 2009, 0910.2325.

Bayesian estimation of switching ARMA models

C. Robert, A. Monfort, Monica Billio, 1999, Journal of Econometrics.

Noninformative Bayesian testing and neutral Bayes factors

C. Robert, N. Caron, 1996 .

The Bayesian choice

Christian P. Robert, C. Robert, 1994 .

Minimum variance importance sampling via Population Monte Carlo

R. Douc, C. Robert, J. Marin, 2007 .

Rethinking the Effective Sample Size

C. Robert, V. Elvira, L. Martino, 2018, International Statistical Review.

Bayesian Modelling and Inference on Mixtures of Distributions

Jean-Michel Marin, Christian P. Robert, Kerrie Mengersen, 2005 .

Estimating Mixtures of Regressions

C. Robert, M. Hurn, A. Justel, 2003 .

MCMC Control Spreadsheets for Exponential Mixture Estimation

C. Robert, A. Philippe, M. Gruet, 1999 .

A Mixture Approach to Bayesian Goodness of Fit

C. Robert, J. Rousseau, 2002 .

Mixture models, latent variables and partitioned importance sampling

G. Casella, C. Robert, M. Wells, 2004 .

Generalized Accept-Reject sampling schemes

G. Casella, C. Robert, M. Wells, 2004 .

Model choice in generalised linear models: A Bayesian approach via Kullback-Leibler projections

C. Robert, C. Goutis, 1998 .

The Most Human Human

C. Robert, 2014 .

Intrinsic losses

C. Robert, 1996 .

Bayes Then and Now

G. Casella, C. Robert, 2004 .

Bayesian Modeling Using WinBUGS

C. Robert, I. Ntzoufras, 2009 .

A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data

George Casella, Christian Robert, G. Casella, 2008, 0808.2902.

Discretization of Continuous Markov Chains and Markov Chain Monte Carlo Convergence Assessment

C. Robert, C. Guihenneuc‐Jouyaux, 1998 .

ABC likelihood-free methods for model choice in Gibbs random fields

C. Robert, J. Marin, A. Grelaud, 2008, 0807.2767.

Deviance information criteria for missing data models

C. Robert, D. M. Titterington, G. Celeux, 2006 .

On perfect simulation for some mixtures of distributions

Christian P. Robert, D. M. Titterington, James P. Hobert, 1999, Stat. Comput..

Inference in model-based cluster analysis

Adrian E. Raftery, Christian P. Robert, Gilles Celeux, 1997, Stat. Comput..

MCMC Specifics for Latent Variable Models

Christian P. Robert, C. Robert, 1998, COMPSTAT.

Invertible Flow Non Equilibrium sampling

Sylvain Le Corff, A. Doucet, É. Moulines, 2021, 2103.10943.

Bayesian Inference on Mixtures of Distributions

Jean-Michel Marin, Christian P. Robert, Kerrie Mengersen, 2008, 0804.2413.

Approximating the marginal likelihood in mixture models

Christian Robert, C. Robert, J. Marin, 2007 .

Discussion on the paper by Brooks, Giudici and Roberts

Petros Dellaportas, Christophe Andrieu, Arnaud Doucet, 2003 .

Weakly Informative Reparameterizations for Location-Scale Mixtures

Christian P. Robert, Kaniav Kamary, Jeong Eun Lee, 2016, Journal of Computational and Graphical Statistics.

“Not Only Defended But Also Applied”: The Perceived Absurdity of Bayesian Inference

Christian P. Robert, Andrew Gelman, C. Robert, 2010, 1006.5366.

Harold Jeffreys’s Theory of Probability Revisited

C. Robert, N. Chopin, J. Rousseau, 2008, 0804.3173.

The Metropolis–Hastings Algorithm

C. Robert, 2015, 1504.01896.

Choice Among Hypotheses Using Estimation Criteria

C. Robert, C. Goutis, 1997 .

Approximate Bayesian Computation in State Space Models

C. Robert, B. McCabe, G. Martin, 2014, 1409.8363.

Properties of nested sampling

C. Robert, N. Chopin, 2008, 0801.3887.

Model Selection for Mixture Models – Perspectives and Strategies

C. Robert, G. Celeux, Sylvia Fruewirth-Schnatter, 2018, Handbook of Mixture Analysis.

A mixture representation of π with applications in Markov chain Monte Carlo and perfect sampling

C. Robert, J. Hobert, 2004, math/0407120.

Testing hypotheses via a mixture estimation model

C. Robert, K. Mengersen, J. Rousseau, 2014, 1412.2044.

Understanding Computational Bayesian Statistics

C. Robert, William M. Boldstad, 2009 .

Contemplating Evidence: properties, extensions of, and alternatives to Nested Sampling

C. Robert, N. Chopin, 2008 .

Logicomix: An Epic Search for Truth

Christian P. Robert, C. Robert, 2012 .

THE SIMULATED LIKELIHOOD RATIO METHOD

C. Robert, A. Monfort, Monica Billio, 2002 .

THE SIMULATED LIKELIHOOD RATIO ( SLR ) METHODM

C. Robert, A. Monfort, Monica Billio, 1998 .

Bayesian modelling and inference difficulties on mixtures of distributions

Jean-Michel Marin, Kerrie Mengersen, Christian Robert, 2004 .

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 .

ABC methods for model choice in Gibbs random fields

Jean-Michel Marin, Aude Grelaud, Christian P. Robert, 2008 .

Maximum likelihood estimation under order restrictions by the prior feedback method

Christian P. Robert, C. Robert, J. T. Hwang, 1996 .

Dicussion on the meeting on ‘Statistical approaches to inverse problems’

Simon J. Godsill, Dan Cornford, Eric Moulines, 2004 .

Discretization and MCMC Convergence Assessment (Lecture Notes in Statistics Vol. 135)

C. Robert, Rbl, 1999 .

On resolving the Savage–Dickey paradox

Jean-Michel Marin, Christian Robert, C. Robert, 2009, 0910.1452.

Discussion on the paper by Wakefield

S. Fienberg, J. Besag, C. Robert, 2004 .

About Incoherent Inference

C. Robert, 2010, 1006.3854.

Do we need an integrated Bayesian/likelihood inference?

C. Robert, A. Gelman, J. Rousseau, 2010 .

The Search for Certainty: A Critical Assessment

Christian P. Robert, C. Robert, 2010, 1001.5109.

Bayesian-Optimal Design via Interacting Particle Systems

Christian P. Robert, Frédéric Y. Bois, Eric Parent, 2006 .

Jeffreys priors for mixture estimation

Christian Robert, Clara Grazian, C. Robert, 2015, 1511.03145.

Non-informative reparameterisations for location-scale mixtures

C. Robert, K. Kamary, J. Lee, 2016 .

Thème COG — Systèmes cognitifs

Jean-Michel Marin, Christian Robert, Alessandra Iacobucci, 2006 .

Three discussions on model choice

C. Robert, 2006 .

Variable selection in qualitative models via an entropic explanatory power

Christian P. Robert, C. Robert, J. Dupuis, 2003 .

Rao-Biackwellization of Sampling Schemes

G. Casella, C. Robert, 2012 .

Discussion on the paper by Kong, McCullagh, Meng, Nicolae and Tan

S. MacEachern, J. Robins, Y. Ritov, 2003 .

Exact Bayesian Analysis of Mixtures

C. Robert, K. Mengersen, 2010, 1010.6113.

Bayesian estimation of hidden Markov chains: a stochastic implementation

C. Robert, G. Celeux, J. Diebolt, 1993 .

Accelerating Metropolis–Hastings algorithms by Delayed AcceptanceT1

C. Robert, Anthony Lee, Clara Grazian, 2018 .

Generalized Poisson difference autoregressive processes

C. Robert, R. Casarin, Giulia Carallo, 2020, International Journal of Forecasting.

Generalized inverse normal distributions

Christian P. Robert, C. Robert, 1991 .

Eaton's Markov chain, its conjugate partner and P-admissibility

C. Robert, J. Hobert, 1999 .

Componentwise approximate Bayesian computation via Gibbs-like steps

Christian P. Robert, Robin J. Ryder, Julien Stoehr, 2019, Biometrika.

Capture-Recapture Models and Bayesian Sampling

C. Robert, E. George, 1990 .

n ° 2010-32 The Search for Certainty : A Critical Assessment

C. Robert, 2010 .

Comment on: Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference

C. Robert, 2016 .

Some comments about A. Ronald Gallant's "Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference"

C. Robert, 2015, 1502.01527.

Book reviews, Winter 2012

C. Robert, S. Donnet, 2012 .

Is Pitman Closeness a Reasonable Criterion

Christian P. Robert, William E. Strawderman, C. Robert, 1993 .

Infering population history with DIY ABC: a user-friendly approach to Approximate Bayesian Computation

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2008 .

Bayesian model comparison in cosmology with Population Monte Carlo

O. Cappé, C. Robert, J. Cardoso, 2009, 0912.1614.

Comments on Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein"

C. Robert, H. Rue, N. Chopin, 2009, 0911.0985.

ESTIMATION OF QUADRATIC FUNCTIONS: NONINFORMATIVE PRIORS FOR NON-CENTRALITY PARAMETERS

James O. Berger, Christian P. Robert, Anne Philippe, 1998 .

A Paradox in Decision-Theoretic Interval Estimation

Christian P. Robert, George Casella, Jiunn Tzon Hwang, 1990 .

A note on the confidence properties of reference priors for the calibration model

C. Robert, A. Philippe, 1998 .

Evidence and Evolution: A Review

C. Robert, 2010, 1004.5074.

Reading Keynes' Treatise on Probability

Christian P. Robert, C. Robert, 2010, 1003.5544.

Reading Theorie Analytique des Probabilites

C. Robert, 2012, 1203.6249.

Subsampling Weakly Dependent Times Series and Application to Extremes

C. Robert, P. Doukhan, Silika Prohl, 2010, 1009.0805.

MCMC Convergence Diagnostics : A « Reviewww »

Christian P. Robert, Christian P. Robert, Kerrie Mengersen, 1998 .

Modified Bessel functions and their applications in probability and statistics

Christian P. Robert, C. Robert, 1990 .

Introducing Monte Carlo methods with

Christian P. Robert, George Casella, G. Casella, 2013 .

How Principled and Practical Are Penalised Complexity Priors

C. Robert, J. Rousseau, 2017 .

Many perspectives on Deborah Mayo's "Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars"

Ron S. Kenett, C. Robert, A. Owen, 2019, 1905.08876.

ABC Methods for Bayesian Model Choice

Christian P. Robert, C. Robert, 2011 .

Minimum variance importance sampling via Population

R. Douc, C. Robert, J. Marin, 2005 .

Bayesian Estimation of Small Effects in Exercise and Sports Science

C. Robert, K. Mengersen, D. Pyne, 2016, PloS one.

Book reviews, Fall 2011

C. Robert, 2011 .

Evidence estimation in finite and infinite mixture models and applications

C. Robert, J. Rousseau, Adrien Hairault, 2022, 2205.05416.

Decision-Theoretic Foundations

Christian P. Robert, C. Robert, 2007 .

Monte Carlo Methods

Christian P. Robert, C. Robert, 2016 .

50 shades of Bayesian testing of hypotheses

C. Robert, 2022, 2206.06659.

Monte Carlo Optimization

G. Casella, C. Robert, 2010 .

Adaptive approximate Bayesian computation

C. Robert, J. Marin, M. Beaumont, 2008, 0805.2256.

The Bayesian choice

C. Robert, 1994 .

Random Variable Generation

G. Casella, C. Robert, 2010 .

(2019). Accelerating Metropolis-Hastings algorithms by Delayed Acceptance. Foundations of Data Science , 1 (2), 103-128.

C. Robert, Anthony Lee, Clara Grazian, 2019 .

Computing Bayes: From Then ‘Til Now

David T. Frazier, C. Robert, G. Martin, 2022, Statistical Science.

The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne

Christian P. Robert, C. Robert, 2012 .

Bayesian Essentials With R 2nd Edition

Christian P. Robert, Jean-Michel Marin, C. Robert, 2020 .

ABC random forests for Bayesian parameter inference

C. Robert, J. Marin, A. Estoup, 2016, Bioinform..

Simulation of truncated normal variables

C. Robert, 2009, 0907.4010.

50 shades of Bayesian testing of hypotheses

C. Robert, 2022, 2206.06659.

Discussion on the paper by Spiegelhalter, Best, Carlin and van der Linde

Aki Vehtari, Christian P. Robert, Matilde Trevisani, 2002 .

Some difficulties with some posterior probability approximations

C. Robert, J. Marin, 2008 .

The Importance Markov Chain

R. Douc, C. Robert, Charly Andral, 2022, ArXiv.

Sampling using Adaptive Regenerative Processes

C. Robert, G. Roberts, M. Pollock, 2022, 2210.09901.

Rao-Blackwellization of Generalized Accept-Reject Schemes

G. Casella, C. Robert, M. Wells, 2000 .

Computing Bayes: From Then ‘Til Now 1

David T. Frazier, C. Robert, G. Martin, 2022 .

Statistics for Spatio-Temporal Data

Christian P. Robert, C. Robert, 2014 .

Discussion of "Sure independence screening for ultra-high dimensional feature space" by Fan and Lv.

C. Robert, 2008 .

On some di(cid:14)culties with a posterior probability approximation technique

C. Robert, J. Marin, 2008 .

Bayesian computation: a summary of the current state, and samples backwards and forwards

C. Robert, P. Green, M. Pereyra, 2015, Statistics and Computing.

Error and inference: an outsider stand on a frequentist philosophy

C. Robert, 2012, Theory and Decision.

Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer

C. Robert, J. Rousseau, 2016, 1610.02503.

Approximate Bayesian computational methods

Robin J. Ryder, C. Robert, J. Marin, 2011, Statistics and Computing.

Adaptive approximate Bayesian computation

C. Robert, J. Marin, M. Beaumont, 2008, 0805.2256.

Computational Advances for and from Bayesian Analysis

A. Doucet, C. Robert, C. Andrieu, 2004 .

Coordinate sampler: a non-reversible Gibbs-like MCMC sampler

C. Robert, Changye Wu, 2019, Statistics and Computing.

Abandon Statistical Significance

David Gal, Andrew Gelman, Christian Robert, 2017, The American Statistician.

On the Jeffreys-Lindley Paradox

Christian P. Robert, C. Robert, 2014, Philosophy of Science.

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data

Aki Vehtari, Andrew Gelman, John P. Cunningham, 2014, J. Mach. Learn. Res..

Estimation of a normal mixture model through Gibbs sampling and Prior Feedback

C. Robert, C. Soubiran, 1993 .

Better together? Statistical learning in models made of modules

Christian P. Robert, Pierre E. Jacob, Lawrence M. Murray, 2017, 1708.08719.

SEMI-TAIL UPPER BOUNDS ON ADMISSIBLE ESTIMATORS IN CONTINUOUS EXPONENTIAL FAMILIES WITH A NUISANCE PARAMETER

C. Robert, Anne-Marie Fraisse, Madeleine Roy, 1998 .

Evidence and Evolution: The logic behind the science

Christian P Robert, C. Robert, 2011, Human Genomics.

Accelerating MCMC algorithms

Christian P. Robert, Víctor Elvira, Changye Wu, 2018, Wiley interdisciplinary reviews. Computational statistics.

Issues in Designing Hybrid Algorithms

Ross McVinish, Kerrie Mengersen, Christian Robert, 2011, 1111.2609.

Introducing Monte Carlo Methods with R

Christian P. Robert, George Casella, G. Casella, 2009 .

Approximate Bayesian computational methods

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

Nonparametric Bayesian Clay for Robust Decision Bricks

Christian P. Robert, Judith Rousseau, C. Robert, 2016 .

Some comments about James Watson's and Chris Holmes' "Approximate Models and Robust Decisions"

C. Robert, J. Rousseau, 2016, 1603.09088.

The expected demise of the Bayes factor

Christian P. Robert, C. Robert, 2015, 1506.08292.

A Message from the Editor

Laurence D. Reed, C. Robert, 2008 .

Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images

Christian P. Robert, Kerrie L. Mengersen, J. M. Thompson, 2007, Comput. Stat. Data Anal..

An overview of robust Bayesian analysis

James O. Berger, Christian P. Robert, Larry Wasserman, 1994 .

Hierarchical Bayesian Methods in Ecology

C. Robert, D. Goodsman, 2010 .

Approximating the Likelihood in ABC

Christopher C. Drovandi, Christian P. Robert, Kerrie Mengersen, 2018, Handbook of Approximate Bayesian Computation.

Large-scale inference

Christian P. Robert, C. Robert, Bradley Efron, 2010 .

In defence of model‐based inference in phylogeography

Jukka Corander, Mark A Beaumont, Rasmus Nielsen, 2010, Molecular ecology.

On the Relevance of the Bayesian Approach to Statistics

Christian P. Robert, C. Robert, 2009, Review of Economic Analysis.

Approximating the likelihood in approximate Bayesian computation

Kerrie Mengersen, Christopher C Drovandi, Clara Grazian, 2018, 1803.06645.

Bayesian computation via empirical likelihood

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

Approximate Bayesian computation via empirical likelihood

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

Bayesian Inference on Finite Mixtures of Distributions

Jean-Michel Marin, Christian P. Robert, Kerrie Mengersen, 2009 .

Beyond the Bayes Factor, A New Bayesian Paradigm for Handling Hypothesis Testing

Christian P. Robert, C. Robert, 2016 .

Bayesian Computational Tools

Christian P. Robert, C. Robert, 2013, 1304.2048.

Jeffreys priors for mixture estimation: Properties and alternatives

Christian P. Robert, Clara Grazian, C. Robert, 2017, Comput. Stat. Data Anal..

Model misspecification in approximate Bayesian computation: consequences and diagnostics

Christian P. Robert, Judith Rousseau, David T. Frazier, 2020, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

ABC model choice via random forests

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

Reparameterisation issues in mixture modelling and their bearing on MCMC algorithms

C. Robert, K. Mengersen, 1999 .

Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation

Christian P. Robert, D. M. Titterington, C. Robert, 1998, Stat. Comput..

Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Jean-Michel Marin, Christian P. Robert, C. Robert, 2010 .

Approximating Bayes in the 21st Century

Christian P. Robert, David T. Frazier, Gael M. Martin, 2021, Statistical Science.

Likelihood-Free Model Choice

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

Adaptive importance sampling in general mixture classes

Jean-Michel Marin, Christian P. Robert, Olivier Cappé, 2007, Stat. Comput..

Coordinate sampler: a non-reversible Gibbs-like MCMC sampler

Christian P. Robert, Changye Wu, C. Robert, 2018, Stat. Comput..

Perfect simulation of positive Gaussian distributions

Christian P. Robert, Anne Philippe, C. Robert, 2003, Stat. Comput..

ABC model choice via random forests

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

Efficient learning in ABC algorithms

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

Lack of confidence in approximate Bayesian computation model choice

Christian P Robert, Jean-Marie Cornuet, Jean-Michel Marin, 2011, Proceedings of the National Academy of Sciences.

Lack of confidence in ABC model choice

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2011, 1102.4432.

Why approximate Bayesian computational (ABC) methods cannot handle model choice problems

Jean-Michel Marin, Natesh S. Pillai, Christian Robert, 2011, 1101.5091.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2008, Bioinform..

Iterated and Sequential Importance Sampling

Christian P. Robert, George Casella, G. Casella, 2004 .

Confidence bands for Brownian motion and applications to Monte Carlo simulation

Jean-Michel Marin, Christian P. Robert, Wilfrid S. Kendall, 2007, Stat. Comput..

Brownian confidence bands on Monte Carlo output

C. Robert, J. Marin, W. Kendall, 2004 .

A Bayesian reassessment of nearest-neighbour classification

Jean-Michel Marin, Lionel Cucala, Christian P. Robert, 2008, 0802.1357.

Truth or Truthiness: Distinguishing Fact from Fiction by Learning to Think Like a Data Scientist

Christian P. Robert, C. Robert, 2018 .

Incoherent phylogeographic inference

Christian P Robert, Adrian E Raftery, Stephen E Fienberg, 2010, Proceedings of the National Academy of Sciences.

Model choice versus model criticism

Christian P. Robert, Kerrie Mengersen, Carla Chen, 2009, Proceedings of the National Academy of Sciences.

Convergence of Adaptive Sampling Schemes

R. Douc, A. Guillin, J.-M. Marin, 2007, 0708.0711.

On Consistency of Approximate Bayesian Computation

Christian P. Robert, David T. Frazier, Gael M. Martin, 2015, 1508.05178.

A vanilla Rao--Blackwellization of Metropolis--Hastings algorithms

Christian P. Robert, Randal Douc, R. Douc, 2009, 0904.2144.

Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modeling

C. Robert, J. Hobert, Vivekananda Roy, 2009, 0911.4546.

Stochastic Modelling for Systems Biology (second edition)

Christian P. Robert, Sophie Donnet, C. Robert, 2012 .

Using a Markov Chain to Construct a Tractable Approximation of an Intractable Probability Distribution

Christian P. Robert, James P. Hobert, Galin L. Jones, 2006 .

Revised evidence for statistical standards

Christian P Robert, Andrew Gelman, C. Robert, 2014, Proceedings of the National Academy of Sciences.

Bayesian Core: The Complete Solution Manual

Jean-Michel Marin, Christian P. Robert, C. Robert, 2009, 0910.4696.

Approximate Bayesian computation with the Wasserstein distance

Mathieu Gerber, Pierre E. Jacob, Christian P. Robert, 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

On parameter estimation with the Wasserstein distance

Christian P. Robert, Mathieu Gerber, Espen Bernton, 2017, Information and Inference: A Journal of the IMA.

Regression and Variable Selection

C. Robert, J. Marin, 2014 .

A special issue on Bayesian inference: challenges, perspectives and prospects

C. Robert, J. Rousseau, 2023, Philosophical Transactions of the Royal Society A.

Handbook of Mixture Analysis

Christian P. Robert, Gilles Celeux, Sylvia Frühwirth-Schnatter, 2018 .

CosmoPMC: Cosmology Population Monte Carlo

Gersende Fort, Simon Prunet, Christian P. Robert, 2011, 1101.0950.

Bayesian Ideas and Data Analysis

Christian P. Robert, Ronald Christensen, Adam J. Branscum, 2012 .

Bayesian Inference

C. Robert, J. Rousseau, J. Marin, 2010, 1002.2080.

Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin

Christian P. Robert, Judith Rousseau, Andrew Gelman, 2010, 1012.2184.

\Not only defended but also applied": A look back at Feller's take on Bayesian inference

C. Robert, A. Gelman, 2010 .

Iterated importance sampling in missing data problems

Jean-Michel Marin, Christian P. Robert, Gilles Celeux, 2006, Comput. Stat. Data Anal..

Approximating Bayes in the 21st Century

David T. Frazier, C. Robert, G. Martin, 2021, Statistical Science.

Living on the Edge: An Unified Approach to Antithetic Sampling

Christian P. Robert, Roberto Casarin, Radu V. Craiu, 2021, 2110.15124.

Computing Bayes: Bayesian Computation from 1763 to the 21st Century

Christian P. Robert, David T. Frazier, Gael M. Martin, 2020, 2004.06425.

On Bayesian Data Analysis

Christian P. Robert, Judith Rousseau, C. Robert, 2010, 1001.4656.

Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale pdf

Christian P. Robert, Julien Stoehr, Changye Wu, 2018, ArXiv.

Subjective Hierarchical Bayes Estimation of a Multivariate Normal Mean: On the Frequentist Interface

James O. Berger, Christian P. Robert, C. Robert, 1990 .

Bayesian computation: a summary of the current state, and samples backwards and forwards

Christian P. Robert, Peter Green, Marcelo Pereyra, 2015, Statistics and Computing.

Monte Carlo Integration

Christian P. Robert, George Casella, G. Casella, 2010 .

Bayesian Essentials with R

Jean-Michel Marin, Christian P. Robert, C. Robert, 2013 .

The Simulated Likelihood Ratio (SLR) Method

Alain Monfort, Christian P. Robert, Monica Billio, 1998 .

Machine Learning, a Probabilistic Perspective

Christian P. Robert, C. Robert, 2014 .

Computational Solutions for Bayesian Inference in Mixture Models

Jean-Michel Marin, Christian P. Robert, Gilles Celeux, 2018, Handbook of Mixture Analysis.

Are Risk Averse Agents More Optimistic? A Bayesian Estimation Approach

Clotilde Napp, Jean-Michel Marin, Selima Ben Mansour, 2007 .

Are risk averse agents more optimistic

C. Robert, J. Marin, E. Jouini, 2005 .

Two discussions of the paper "Bayesian measures of model complexity and fit" by D. Spiegelhalter et al., Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, March 13th, 2002

E. Moreno, F.-J. Vazquez-Polo, C. P. Robert, 2013, 1310.2905.

Rao–Blackwellisation in the Markov Chain Monte Carlo Era

C. Robert, G. Roberts, 2021, International Statistical Review.

Adaptive Multiple Importance Sampling

Jean-Marie Cornuet, Jean-Michel Marin, Christian P. Robert, 2009, 0907.1254.

MINIMUM VARIANCE IMPORTANCE SAMPLING VIA POPULATION MONTE

A. Guillin, C. P. Robert, R. Douc, 2007 .

From Prior Information to Prior Distributions

Christian P. Robert, C. Robert, 2007 .

Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation

Jean-Michel Marin, Christian P. Robert, Gilles Celeux, 2010, 1010.0300.

The Theory That Would Not Die: Hardcover: 320+xiv pages Publisher: Yale University Press (first edition, May 2011) Language: English ISBN-10: 0300169698

Christian P. Robert, Sharon Bertsch McGrayne, C. Robert, 2012 .

Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models

David T. Frazier, Christian P. Robert, Gael M. Martin, 2016, Journal of Computational and Graphical Statistics.

Using Parallel Computation to Improve Independent Metropolis–Hastings Based Estimation

Christian P. Robert, Pierre Jacob, Murray H. Smith, 2010, ArXiv.

Riemann sums for MCMC estimation and convergence monitoring

Christian P. Robert, Anne Philippe, C. Robert, 2001, Stat. Comput..

Recycling rejected values in accept-reject methods

Christian P. Robert, George Casella, G. Casella, 1995 .

Improved Confidence Estimators for the Usual Multivariate Normal Confidence Set

G. Casella, C. Robert, 1989 .

Improved Confidence Statements for the Usual Multivariate Normal Confidence Set

G. Casella, C. Robert, 1994 .

Rao-Blackwellization in the MCMC era

C. Robert, G. Roberts, 2021, 2101.01011.

Bayesian computation for statistical models with intractable normalizing constants

Christian P. Robert, Nicolas Lartillot, Yves Atchade, 2008, 0804.3152.

Bayesian computation: a perspective on the current state, and sampling backwards and forwards

Christian P. Robert, Marcelo Pereyra, Peter J. Green, 2015, 1502.01148.

Mixtures: Estimation and Applications

Kerrie Mengersen, Christian P. Robert, D. M. Titterington, 2011 .

Statistica Sinica Preprint No : SS13-338 R 2 Title Objective Bayesian hypothesis testing in binomial regression models with integral prior distributions

Diego Salmeron, Juan Antonio Cano, C. P. Robert, 2014 .

The Seven Pillars of Statistical Wisdom

Christian P. Robert, C. Robert, 2018 .

Relevant statistics for Bayesian model choice

J.-M. Marin, C. P. Robert, N. Pillai, 2011, 1110.4700.

Approximate Bayesian Computation: A Survey on Recent Results

Christian P. Robert, C. Robert, 2014, MCQMC.

Bayesian Decision Analysis: Principles and Practice by Jim Q. Smith: A review

Christian P. Robert, C. Robert, 2011 .

Computational Methods for Numerical Analysis with R

Christian P. Robert, C. Robert, 2019, CHANCE.

Intrinsic credible regions : On objective Bayesian approach to interval estimation

Christian P. Robert, C. Robert, 2006 .

Independent Random Sampling Methods

Christian P. Robert, C. Robert, 2019, CHANCE.

Accelerating Metropolis-Hastings algorithms by Delayed Acceptance

Anthony Lee, Christian P. Robert, Clara Grazian, 2015, Foundations of Data Science.

Adaptive importance sampling in general mixture classes

O. Cappé, R. Douc, C. Robert, 2007, Stat. Comput..

Naked Statistics: Stripping the Dread from Data

Christian P. Robert, C. Robert, 2014 .

The Importance Markov Chain

R. Douc, C. Robert, Charly Andral, 2022, ArXiv.

Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching

Christian P. Robert, Clara Grazian, Marco Banterle, 2014, 1406.2660.

Jeffreys Priors for Mixture Models

C. Robert, C. Grazian, 2014 .

On the Jeffreys-Lindley's paradox

Christian Robert, C. Robert, 2013, 1303.5973.

Application of ABC to Infer the Genetic History of Pygmy Hunter-Gatherer Populations from Western Central Africa

C. Robert, J. Marin, J. Cornuet, 2018, Handbook of Approximate Bayesian Computation.

Bayes Estimators Associated with Uniform Distributions on Spheres (II): the Hierarchical Bayes Approach

Christian P. Robert, Mary Ellen Bock, George Casella, 1989 .

Population Monte Carlo

O. Cappé, C. Robert, J. Marin, 2004 .

Model Misspecification in ABC: Consequences and Diagnostics

Christian P. Robert, Judith Rousseau, David T. Frazier, 2017, 1708.01974.

Insufficient Gibbs Sampling

Robin J. Ryder, C. Robert, A. Luciano, 2023, 2307.14973.

Error and inference: an outsider stand on a frequentist philosophy

Christian P. Robert, C. Robert, 2011, 1111.5827.

Estimation of Accuracy in Testing

Christian P. Robert, George Casella, Martin T. Wells, 1992 .

New Perspectives on Linear Calibration

C. Robert, T. Kubokawa, 1994 .

Magical Mathematics: The Mathematical Ideas That Animate Great Magic Tricks

Christian P. Robert, C. Robert, 2013 .

Average of Recentered Parallel MCMC for Big Data

Christian P. Robert, Changye Wu, C. Robert, 2017, 1706.04780.

On some accurate bounds for the quantiles of a non-central chi squared distribution☆

Christian P. Robert, C. Robert, 1990 .

Pragmatics of Uncertainty

Christian P. Robert, C. Robert, 2019, CHANCE.

Reliable ABC model choice via random forests

C. Robert, J. Marin, J. Cornuet, 2014, Bioinform..

Computing Bayes: From Then ‘Til Now

David T. Frazier, C. Robert, G. Martin, 2022, Statistical Science.

Markov Chain Monte Carlo Methods, Survey with Some Frequent Misunderstandings

Christian P. Robert, Wu Changye, C. Robert, 2020, Wiley StatsRef: Statistics Reference Online.

Simulation in statistics

Christian P. Robert, C. Robert, 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

Lack of confidence in approximate Bayesian computation model choice

C. Robert, N. Pillai, J. Marin, 2011, Proceedings of the National Academy of Sciences.

Bayesian hypothesis testing as a mixture estimation modelT1

C. Robert, K. Mengersen, J. Rousseau, 2021 .

Book Reviews, Summer 2013

C. Robert, 2013 .

Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods"

Arnaud Doucet, Jean-Michel Marin, Christian P. Robert, 2010, 1011.0834.

On Particle Learning

Jean-Michel Marin, Christian P. Robert, Kerrie Mengersen, 2010, 1006.0554.

Importance Sampling Schemes for Evidence Approximation in Mixture Models

Christian P. Robert, Jeong Eun Lee, C. Robert, 2013, 1311.6000.

BAYESIAN COMPUTATIONAL TOOLS: A BRIEF TUTORIAL

Christian P. Robert, C. Robert, 2006 .

Generalized Bouncy Particle Sampler

Christian P. Robert, Changye Wu, C. Robert, 2017, 1706.04781.

Marginal maximum a posteriori estimation using Markov chain Monte Carlo

Simon J. Godsill, Arnaud Doucet, Christian P. Robert, 2002, Stat. Comput..

Refining Poisson Confidence Intervals

Christian P. Robert, George Casella, G. Casella, 1988 .

Robust shrinkage estimators of the location parameter for elliptically symmetric distributions

C. Robert, D. Cellier, D. Fourdrinier, 1989 .

An Explicit Formula for the Risk of the Positive‐Part James‐Stein Estimator

C. Robert, 1988 .

Improved Confidence Sets in Spherically Symmetric Distributions

G. Casella, C. Robert, 1987 .

Controlled shrinkage estimators (a class of estimators better than the least squares estimator, with respect to a general quadratic loss, for normal observations

C. Robert, D. Cellieb, F. Fordrinier, 1989 .

A lower bound for the risk of classes of shrinkage estimators ina general multivariate estimation problem and some deduced estimators

C. Robert, 1989 .

Jeffreys Priors for Mixture Models Distribuzioni a priori di Jeffreys per i modelli mistura

C. Robert, C. Grazian, 2014 .

Paradoxes in Scientific Inference

Christian P. Robert, C. Robert, 2013 .

Estimation of demo‐genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics

C. Robert, J. Marin, J. Cornuet, 2012, Molecular Ecology Resources.

Iterated importance sampling in missing data problems

C. Robert, G. Celeux, J. Marin, 2006, Computational Statistics & Data Analysis.

On variance stabilisation in Population Monte Carlo by double Rao-Blackwellisation

Jean-Michel Marin, Christian P. Robert, Alessandra Iacobucci, 2010, Comput. Stat. Data Anal..

Convergence of Adaptive Sampling Schemes

Jean-Michel Marin, Christian P. Robert, Randal Douc, 2004 .

Computational and Inferential Difficulties with Mixture Posterior Distributions

C. Robert, G. Celeux, M. Hurn, 2000 .

Asymptotic properties of approximate Bayesian computation

David T. Frazier, C. Robert, J. Rousseau, 2016, Biometrika.

Introducing Monte Carlo Methods with R

G. Casella, C. Robert, 2009 .

Discussion of "Is Bayes Posterior just Quick and Dirty Confidence?" by D. A. S. Fraser

Christian P. Robert, C. Robert, 2011, 1202.0625.

A History of Markov Chain Monte Carlo–Subjective Recollections from Incomplete Data–

G. Casella, C. Robert, David Spiegelhalter, 2008 .

Monte Carlo algorithms for model assessment via conflicting summaries

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

Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method

C. Robert, D. M. Titterington, T. Rydén, 2000 .

Minimum variance importance sampling via Population

R. Douc, C. Robert, J. Marin, 2005 .