I) Mini-courses:

• Simon Barthelmé (Gipsa-lab Grenoble)

The Expectation-Propagation Algorithm: a tutorial    (part. 1)  –  (part. 2 )

• Christine Keribin (Université Paris Sud)

Variational Bayes methods and algorithms   (part. 1)  –  (part. 2)

• Jean-Michel Marin (Université Montpellier)

Approximate Bayesian Computation methods for model choice a machine learning point of view   (part. 1)  –  (part. 2)

• Christian P. Robert (Université Paris Dauphine & University of Warwick)

Markov Chain Monte Carlo Methods   (part. 1)  – Rao-Blackwellisation for accelerating Metropolis-Hastings (part. 2)

II) Talks

• Leonardo Bottolo (University of Cambridge)

Bayesian hierarchical model for financial time series (pdf)

• Nicolas Chopin (ENSAE Paris-Tech)

Leave Pima Indians alone (pdf)

• Guillaume Dehaene (Université de Genève)

Expectation Propagation in the large-data limit (pdf)

• Pierre Druilhet (Université Blaise Pascal Clermont-Ferrand)

Convergence modes for prior distributions (pdf)

• Richard Everitt (University of Reading)

Sequential Monte Carlo with estimated likelihoods (pdf)

• Nial Friel (University College Dublin)

Approximate Bayesian inference for Gibbs random fields: noisy MCMC (pdf)

• Pierre Latouche (Université Paris 1 Panthéon-Sorbonne)

Goodness of fit of logistic models for random graphs (pdf)

• Denys Pommeret (Aix-Marseille Université)

Combining ridge parameter with the g-prior of Zellner (pdf)

• Dennis Prangle (Newcastle University)

A data augmentation approach to high dimensional ABC (pdf)

• Pierre Pudlo (Aix-Marseille Université)

Adaptive multiple importance sampling (pdf)

• Sylvia Richardson (MRC Biostatistics Unit)

Exploring the presence of complex dependence structures in epidemiological and genomic data through flexible clustering (pdf)

• James Ridgway (ENSAE ParisTech)

On the properties of variational approximations of Gibbs posteriors (pdf)

• Stéphane Robin (AgroParisTech)

Exact Bayesian inference for some models with discrete parameters (pdf)

• Judith Rousseau (ENSAE ParisTech)

Nonparametric mixture models with finite state space (pdf)

• Daniel Rudolf (Friedrich-Schiller-University of Jena)

Approximations of geometrically ergodic Markov chains (pdf)

• Adeline Samson (Université Joseph Fourier Grenoble)

Bayesian and Frequentist algorithms to estimate parameters of Stochastic differential equations (pdf)

• Darren Wilkinson (Newcastle University)

Bayesian Hierarchical Modelling of Genetic Interaction in Yeast  (pdf)