Approximation Methods in Bayesian Analysis
Méthodes approchées en analyse statistique bayésienne

19 – 23 June 2023

Scientific Committee & Organizing Committee

Comité scientifique & Comité d’organisation

Marta Catalano (University of Warwick)
Pierre Jacob (ESSEC Business School)
Igor Pruenster (Bocconi University)
Christian Robert (Université Paris Dauphine-PSL)
Veronika Rockova (University of Chicago)
Dario Spanò (University of Warwick)

In recent years Bayesian methods have flourished both in theoretical and applied studies. The sound probabilistic framework, the wide spectrum of sampling strategies and the desirable inferential properties attract researchers with different background and experiences. The aim of this conference is to foster cross-fertilization on the role of approximation and distances in modern Bayesian analysis. This broad subject is declined in five different topics, for each of which we propose a description and a tentative list of speakers:

  1. Methodological advances in posterior approximation;
  2. Bayesian methods when the model is wrong;
  3. Frequentist validation of Bayesian algorithms;
  4. Analysis and approximation of complex dependence structures;
  5. Bayesian inference, models of population dynamics and approximation

Ces dernières décennies, les méthodes bayésiennes ont pris de l’importance, à la fois dans les problèmes théoriques et appliqués. Leur cadre probabiliste cohérent, leur large éventail de stratégies d’échantillonnage et leurs propriétés inférentielles garanties attirent des chercheurs de domaines et d’expériences différents. L’objectif de cette conférence est de favoriser la fertilisation croisée sur le rôle de l’approximation et des diverses notions de distances dans l’implémentation effective de l’analyse bayésienne moderne. Ce vaste sujet se décline en cinq thèmes distincts, pour chacun desquels nous proposons une description et une liste indicative d’intervenant(e)s :

  1. Avancées méthodologiques en approximation de la loi a posteriori ;
  2. Méthodes bayésiennes sous un modèle mal spécifié ;
  3. Validation fréquentiste d’algorithmes bayésiens ;
  4. Analyse et approximation de structures de dépendance complexes ;
  5. Inférence bayésienne, modèles de dynamique des populations et approximation


Julia Adela Palacios (Standford University)   Novel coalescent and phylogenetic modeling strategies for Bayesian inference in phylodynamics
Julyan Arbel (Université Grenoble Alpes)   Rapture of the deep: highs and lows of Bayes in a world of depths
Morgane Austern (Harvard University)    Transport distances and novel concentration inequalities
Mark Beaumont (University of Bristol)   Likelihood-free inference of selection and population demography using whole-genome data
Federico Camerlenghi (University of Milano – Bicocca)   Normalized random measures with interacting atoms for Bayesian nonparametric mixtures
Trevor Campbell (University of British Columbia)   Embracing the Chaos: Analysis and Diagnosis of Numerical Instability in Variational Flows
Lorenzo Cappello (Pompeu Fabra University)  Variance change point detection with credible sets
Maria De Iorio (National University of Singapore)   Repulsion, Chaos and Equilibrium in Mixture Models
David Frazier (Monash University)   Generalized Bayesian Inference with Intractable Distances
Andrew Gelman (Columbia University)   The gap between approximate and Bayes: Some ideas and challenges
Subhashis Ghoshal (North Carolina State University)   Optimal Bayesian Inference for High-dimensional Linear Regression Based on Sparse Projection-posterior
Maria Fernanda Gil Leyva Villa (National Autonomous University of Mexico)   Stick-breaking processes with dependent length variables
Chris Holmes (University of Oxford)   Calibrated Bayes and conformal predictions under model misspecification
Paul Jenkins (University of Warwick)   Properties of the Fleming-Viot process and its projections
Jere Koskela (University of Warwick)   Simple criteria for consistent Bayesian tree reconstruction
Hugo Lavenant (Bocconi University)   Quantifying the merging of opinion in Bayesian nonparametrics via optimal transport
Fabrizio Leisen (University of Nottingham)   Exchangeable random measures and beyond
Li Ma (Duke University)   Residual treed Gaussian Processes
Steve MacEachern (The Ohio State University)   Dependent quantile pyramids
Julia-Adela Palacios (Stanford University)    Novel coalescent and phylogenetic modeling strategies for Bayesian inference in phylodynamics
Igor Prünster (Bocconi University)   Short Talks I  II III – Poster Session
Debdeep Pati (Texas A&M University)   Adaptive finite element type decomposition of Gaussian random fields
Kolyan Ray (Imperial College London)   A variational Bayes approach to debiased inference in high-dimensional linear regression
Giovanni Rebaudo (University of Turin)   Graph-Aligned Random Partition Models
Sylvia Richardson (University of Cambridge)   Scaling up Bayesian modelling and computation for real-world biomedical applications
Judith Rousseau (Université Paris Dauphine-PSL)   Empirical Bayes revisited
Matteo Ruggiero (Université Paris Cité)   Approximate filtering via discrete dual processes
Subhabrata Sen (Harvard University)   Mean-field approximations for high-dimensional bayesian regression
Pragya Sur (Harvard University)   A new central limit theorem for the augmented IPW estimator in high dimensions
Surya Tokdar (Duke University)   Bayes in the Extremes
Aad van der Vaart (Delft University of Technology)   Linear methods for nonlinear inverse problems
Andi Wang (University of Warwick)   Explicit convergence bounds for Metropolis Markov chains
Sinead Williamson (University of Texas at Austin)   Nonparametric posterior normalizing flows

Jason Xu (Duke University)    Relaxing constraints via Distance-to-set penalties