Bayesian inference for the exponential random graph model (Nial Friel)
The exponential random graph is arguably the most popular model for the statistical analysis of network data. However despite its widespread use, it i...
StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"
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About
L'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 également apporter une meilleure compréhension des phénomènes observés. Afin de faciliter les contacts entre les différentes communautés et de faire ainsi germer de nouvelles idées, un colloquium d'audience internationale (en langue anglaise) sur le thème «Challenging problems in Statistical Learning» a été organisé à l'Université Bordeaux Segalen les 8 et 9 avril 2013. Vous trouverez ci-dessous les enregistrements des exposés donnés lors de ce colloquium. Recommandé à : étudiant de la discipline, chercheur - Catégorie : cours podcast - Année de réalisation : 2013
Clustering of variables combined with variable selection using random forests : application to gene expression data (Robin Genuer & Vanessa Kuentz-Simonet)
The main goal of this work is to tackle the problem of dimension reduction for highdimensional supervised classification. The motivation is to handle ...
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator (Arnaud Doucet)
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 sampl...
Investigating on nonlinear relationship in high-dimensional setting (Frédéric Ferraty)
The high dimensional setting is a modern and dynamic research area in Statistics. It covers numerous situations where the number of explanatory variab...
Learning with the Online EM Algorithm (Olivier Cappé)
The Online Expectation-Maximization (EM) is a generic algorithm that can be used to estimate the parameters of latent data models incrementally from l...
Modular priors for partially identified models (Ioanna Manolopoulou)
This work is motivated by the challenges of drawing inferences from presence-only data. For example, when trying to determine what habitat sea-turtles...
New challenges for (biological) network inference with sparse Gaussian graphical models (Julien Chiquet)
Network inference methods based upon sparse Gaussian Graphical Models (GGM) have recently emerged as a promising exploratory tool in genomics. They gi...
Regularized PCA to denoise and visualize data (Julie Josse)
Principal component analysis (PCA) is a well-established method commonly used to explore and visualize data. A classical PCA model is the fixed effect...
Strategies to analyze (Benoît Liquet)
Recent technological advances in molecular biology have given rise to numerous large scale datasets whose analysis have risen serious methodological c...