Week 5: Bayesian Statistics and Algorithms
February 29 – March 4, 2016
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This week will take place during the Thematic Month on « Statistics » at CIRM (Marseille, France). It will be devoted to Bayesian statistics as well as their associated algorithms.
Theoretical presentations and applications on these two themes will be developed. Mini courses will also take place and theoretical and applied aspects of the Bayesian approach will be presented. The algorithms will include ABC (Approximate Bayesian Computation) and MCMC algorithms. Some key words regarding this workshop: – Bayesian statistics (courses and lectures) Note: all talks and mini-courses will be in English |
Scientific Committee
Nicolas Chopin (ENSAE ParisTech) Organizing Committee Thibaut Le Gouic (Ecole Centrale de Marseille) Speakers I) Mini-courses
Expectation-Propagation for Approximate Inference
Variational Bayes methods and algorithms
Computational Bayesian statistics II) Talks
Bayesian hierarchical mixture model for financial time series
Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation
Expectation Propagation is exact in the large-data limit
Convergence modes for prior distributions
An overview of noisy MCMC and SMC
Accelerating Bayesian inference for intractable likelihood models using noisy MCMC
Goodness of fit of logistic models for random graphs
Combining ridge parameter with the g-prior of Zellner
A data augmentation approach to high dimensional ABC
Adaptive multiple important sampling
Exploring the presence of complex dependence structures in epidemiological and genomic data
On the properties of variational approximations of Gibbs posteriors
Exact Bayesian inference for some models with discrete parameters
Nonparametric mixture models and HMMS
Approximations of geometrically ergodic Markov chains
Computational methods for stochastic differential equations
Bayesian Hierarchical Modelling of Genetic Interaction in Yeast |