Bayes in Grenoble: Vadim Strijov
on the February 19, 2020
February, 19 at 10 am
Vadim Strijov from Moscow Institute of Physics and Technology, Russia, will talk about "Bayesian model selection and multimodelling". The event will take place on February, 19 at 10:00 am at Inria Montbonnot.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.
The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk. https://sites.google.com/view/bigseminar/accueil
React on social media: #BIGseminar
Bayesian model selection and multimodelling
Abstract
Multimodeling for learning-to-learn or meta-learning are discussed. The talk defines Bayesian strategies for local and universal model selection and multimodellings and discusses the principles of model selection. Multimodels are used when a sample cannot be described by a single model. This happens when feature weights depend on the feature values. Though a multimodel is an interpretable generalization of a single model case, it can contain large number of similar models. Pruning algorithms are constructed based on the suggested method for statistical model comparison.
The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk. https://sites.google.com/view/bigseminar/accueil
React on social media: #BIGseminar
19 February 2020, Vadim Strijov (Moscow Institute of Physics and Technology, Russia)
Bayesian model selection and multimodelling
AbstractMultimodeling for learning-to-learn or meta-learning are discussed. The talk defines Bayesian strategies for local and universal model selection and multimodellings and discusses the principles of model selection. Multimodels are used when a sample cannot be described by a single model. This happens when feature weights depend on the feature values. Though a multimodel is an interpretable generalization of a single model case, it can contain large number of similar models. Pruning algorithms are constructed based on the suggested method for statistical model comparison.
Published on February 5, 2020
Practical informations
Location
F107, Inria Grenoble Rhône-Alpes (Montbonnot building)