Principal component analysis (PCA) is a well-established method commonly used to explore and visualize data. A classical PCA model is the fixed effect model where data are generated as a fixed structure of low rank corrupted by noise. Under thi

New challenges for (biological) network inference with sparse Gaussian graphical models (Julien Chiquet)

May 16th, 2013 58m 6s

Network inference methods based upon sparse Gaussian Graphical Models (GGM) have recently emerged as a promising exploratory tool in genomics. They give a sounded representation of direct relationships between genes and are accompanied with spa

Learning with the Online EM Algorithm (Olivier Cappé)

May 16th, 2013 1h 6m

The Online Expectation-Maximization (EM) is a generic algorithm that can be used to estimate the parameters of latent data models incrementally from large volumes of data. The general principle of the approach is to use a stochastic approximati

Clustering of variables combined with variable selection using random forests : application to gene expression data (Robin Genuer & Vanessa Kuentz-Simonet)

May 16th, 2013 55m 48s

The main goal of this work is to tackle the problem of dimension reduction for highdimensional supervised classification. The motivation is to handle gene expression data. The proposed method works in 2 steps. First, one eliminates redundancy u

Modular priors for partially identified models (Ioanna Manolopoulou)

May 16th, 2013 49m 34s

This work is motivated by the challenges of drawing inferences from presence-only data. For example, when trying to determine what habitat sea-turtles "prefer" we only have data on where turtles were observed, not data about where the turtles a

Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator (Arnaud Doucet)

May 16th, 2013 48m 50s

When an unbiased estimator of the likelihood is used within an Markov chain Monte Carlo (MCMC) scheme, it is necessary to tradeoff the number of samples used against the computing time. Many samples for the estimator will result in a MCMC schem

Bayesian inference for the exponential random graph model (Nial Friel)

May 16th, 2013 1h 1m

The exponential random graph is arguably the most popular model for the statistical analysis of network data. However despite its widespread use, it is very complicated to handle from a statistical perspective, mainly because the likelihood fun

Investigating on nonlinear relationship in high-dimensional setting (Frédéric Ferraty)

May 16th, 2013 54m 1s

The high dimensional setting is a modern and dynamic research area in Statistics. It covers numerous situations where the number of explanatory variables is much larger than the sample size. This is the case in genomics when one observes (dozen

Strategies to analyze (Benoît Liquet)

May 16th, 2013 57m 42s

Recent technological advances in molecular biology have given rise to numerous large scale datasets whose analysis have risen serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experien

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