StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

Statlearn2013

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 internati...

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StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

Episodes (9)

May 16, 2013 Bayesian inference for the exponential random graph model (Nial Friel)
May 16, 2013 Clustering of variables combined with variable selection using random forests : application to gene expression data (Robin Genuer & Vanessa Kuentz-Simonet)
May 16, 2013 Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator (Arnaud Doucet)

StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

May 16, 2013
1 second
40 MB

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 is intractable for all but trivially small networks. This talk will outline some recent work in this area to overcome this intractability. In particular, we will outline some approaches to carry out Bayesian parameter estimation and show how this can be extended to estimate Bayes factors between competing models.

StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

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

Statlearn2013

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