Bayes in Grenoble: talks at AIP 2019

from July 8, 2019 to July 12, 2019

Bayesian talks will be given at the Applied Inverse Problems conference including those of Botond Szabo (Leiden University), Jean-Bernard Salomond (UPEC), Olivier Zahm (Inria) and Julyan Arbel (Inria).
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

July 8-12 2019, Applied Inverse Problem Conference

Minisymposium-42 Uncertainty Quantification for Nonparametric Inverse Problems

Tuesday 09 July 2019 at 16:30 Room 1 and Friday 12 July 2019 at 08:00 Room 12

 

The aim of the minisymposium is to bring together researchers from statistics and inverse problems community who are working on the growing field of uncertainty quantification for inverse problems. Uncertainty quantification is necessary in inverse problems to assess statistical reliability of the obtained solutions. However, ill-posedness of the underlying model generates challenges that are not typically considered in classical statistics literature.

 
Minisymposium-54 Accelerating Sampling Strategies for Large-Scale Bayesian Inverse Problems

Tuesday 09 July 2019 at 14:00 Room 13 and Tuesday 09 July 2019 at 16:30 Room 13
 

Recent advances in computational techniques are starting to make Bayesian inversion feasible for large scale problems involving partial differential equations (PDEs). However, for some realistic applications, particularly those involving complicated posterior measures (i.e, multi-modal or high-dimensional distributions), or for posterior distributions for which the underlying PDE is computationally demanding (arising in, e.g, seismology or fluid dynamics), the computational cost associated to a Bayesian inversion can still become prohibitively expensive. This mini-symposium aims at exploring recent developments, both theoretical and methodological, that aim at improving the computational complexity of Bayesian inversion procedures.
 

Talks by Botond Szabo, Jean-Bernard Salomond, Julyan Arbel, Olivier Zahm, and others.

 

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Published on June 25, 2019

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