StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"
Statlearn2013L'apprentissage statistique joue de nos jours un rôle croissant dans de nombreux domaines scientifiques et doit de ce fait faire face à des problèmes nouveaux. Il est par conséquent important de proposer des méthodes d'apprentissage statistique adaptées aux problèmes modernes posés par les différents champs d'application. Outre l'importance de la précision des méthodes proposées, elles devront éga... more
| Publishes | Daily | Episodes | 9 | Founded | 12 years ago |
|---|---|---|---|---|---|
| Language |  French | Categories | CoursesEducation | ||
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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 scheme whic... more
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 (dozens of) ... more
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 approximation sch... more
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 this mode... more
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 experience has... more
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 using c... more
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 function ... more
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 sparse in... more
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