**W**hile back in Paris from Harvard in early January, Pierre Jacob will give two talks on works of his:

January 09, 10:30, séminaire d’Analyse-Probabilités, Université Paris-Dauphine: Unbiased MCMC

*Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to infinity. However, MCMC estimators are generally biased after any fixed number of iterations, which complicates both parallel computation and the construction of confidence intervals. We propose to remove this bias by using couplings of Markov chains and a telescopic sum argument, inspired by Glynn & Rhee (2014). The resulting unbiased estimators can be computed independently in parallel, and confidence intervals can be directly constructed from the Central Limit Theorem for i.i.d. variables. We provide practical couplings for important algorithms such as the Metropolis-Hastings and Gibbs samplers. We establish the theoretical validity of the proposed estimators, and study their variances and computational costs. In numerical experiments, including inference in hierarchical models, bimodal or high-dimensional target distributions, logistic regressions with the Pólya-Gamma Gibbs sampler and the Bayesian Lasso, we demonstrate the wide applicability of the proposed methodology as well as its limitations. Finally, we illustrate how the proposed estimators can approximate the “cut” distribution that arises in Bayesian inference for misspecified models. *

January 11, 10:30, CREST-ENSAE, Paris-Saclay: Better together? Statistical learning in models made of modules *[Warning: Paris-Saclay is not in Paris!]*

*In modern applications, statisticians are faced with integrating heterogeneous data modalities relevant for an inference or decision problem. It is convenient to use a graphical model to represent the statistical dependencies, via a set of connected “modules”, each relating to a specific data modality, and drawing on specific domain expertise in their development. In principle, given data, the conventional statistical update then allows for coherent uncertainty quantification and information propagation through and across the modules. However, misspecification of any module can contaminate the update of others. In various settings, particularly when certain modules are trusted more than others, practitioners have preferred to avoid learning with the full model in favor of “cut distributions”. In this talk, I will discuss why these modular approaches might be preferable to the full model in misspecified settings, and propose principled criteria to choose between modular and full-model approaches. The question is intertwined with computational difficulties associated with the cut distribution, and new approaches based on recently proposed unbiased MCMC methods will be described*.

Long enough after the New Year festivities (if any) to be fully operational for them!