834 citations · 3.9k across the 95 of their papers we have counts for
13 papers · 1 filter
Bayesian model comparison in cosmology with Population Monte Carlo
Martin Kilbinger, Darren Wraith, Christian P. Robert +6
We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are c…
Comments on "Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein
Pierre Jacob, Nicolas Chopin, Christian P. Robert +1
This is the compilation of our comments submitted to the Journal of the Royal Statistical Society, Series B, to be published within the discussion of the Read Paper of Andrieu, Dou…
Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modeling
James P. Hobert, Vivekananda Roy, Christian P. Robert
The reversible Markov chains that drive the data augmentation (DA) and sandwich algorithms define self-adjoint operators whose spectra encode the convergence properties of the algo…
Bayesian Core: The Complete Solution Manual
Christian P. Robert, Jean-Michel Marin
This solution manual contains the unabridged and original solutions to all the exercises proposed in Bayesian Core, along with R programs when necessary.
Importance sampling methods for Bayesian discrimination between embedded models
Jean-Michel Marin, Christian P. Robert
This paper surveys some well-established approaches on the approximation of Bayes factors used in Bayesian model choice, mostly as covered in Chen et al. (2000). Our focus here is…
On resolving the Savage-Dickey paradox
Jean-Michel Marin, Christian Robert
The Savage-Dickey ratio is known as a specialised representation of the Bayes factor (O'Hagan and Forster, 2004) that allows for a functional plugging approximation of this quantit…
Model choice versus model criticism
Christian P. Robert, Kerrie L. Mengersen, Carla Chen
The new perspectives on ABC and Bayesian model criticisms presented in Ratmann et al.(2009) are challenging standard approaches to Bayesian model choice. We discuss here some issue…
Monte Carlo Methods in Statistics
Christian P. Robert
Monte Carlo methods are now an essential part of the statistician's toolbox, to the point of being more familiar to graduate students than the measure theoretic notions upon which…
On the relevance of the Bayesian approach to Statistics
Christian P. Robert
We argue here about the relevance and the ultimate unity of the Bayesian approach in a neutral and agnostic manner. Our main theme is that Bayesian data analysis is an effective to…
Computational methods for Bayesian model choice
Christian P. Robert, Darren Wraith
In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we…
Simulation of truncated normal variables
Christian P. Robert
We provide in this paper simulation algorithms for one-sided and two-sided truncated normal distributions. These algorithms are then used to simulate multivariate normal variables…
Adaptive Multiple Importance Sampling
Jean-Marie Cornuet, Jean-Michel Marin, Antonietta Mira +1
The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earli…
A vanilla Rao--Blackwellization of Metropolis--Hastings algorithms
Randal Douc, Christian P. Robert
Casella and Robert [Biometrika 83 (1996) 81--94] presented a general Rao--Blackwellization principle for accept-reject and Metropolis--Hastings schemes that leads to significant de…