Semaine Bayésienne et algorithmes
29 février au 4 mars 2016

I) Mini-cours:

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

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

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

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

II) Exposés:

Bayesian hierarchical model for financial time series (pdf)

Leave Pima Indians alone (pdf)

Expectation Propagation in the large-data limit (pdf)

Convergence modes for prior distributions (pdf)

Sequential Monte Carlo with estimated likelihoods (pdf)

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

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

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

A data augmentation approach to high dimensional ABC (pdf)

Adaptive multiple importance sampling (pdf)

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

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

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

Nonparametric mixture models with finite state space (pdf)

Approximations of geometrically ergodic Markov chains (pdf)

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

Bayesian Hierarchical Modelling of Genetic Interaction in Yeast  (pdf)