Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Collection Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Organizer(s) Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne

Date(s) 15/06/2020 - 19/06/2020

linked URL https://www.cirm-math.com/cirm-virtual-event-2146.html

00:00:00 / 00:00:00

10 25

Hierarchical bayes modeling for large-scale inference

By Daniel Yekutieli

We present a novel theoretical framework for statistical analysis of Large-scale problems that builds on the Robbins compound decision approach. We present a hierarchical Bayesian approach for implementing this framework and illustrate its application to simulated data.

Information about the video

Date of recording 03/02/2020
Date of publication 15/06/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19643703
Cite this video Yekutieli, Daniel (03/02/2020). Hierarchical bayes modeling for large-scale inference. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19643703
URL https://dx.doi.org/10.24350/CIRM.V.19643703

Domain(s)

Statistics Theory

Bibliography

FERGUSON, Thomas S., et al. Prior distributions on spaces of probability measures. The annals of statistics, 1974, vol. 2, no 4, p. 615-629. - https://www.jstor.org/stable/2958401
ROBBINS, Herbert, et al. Asymptotically subminimax solutions of compound statistical decision problems. In : Proceedings of the second Berkeley symposium on mathematical statistics and probability. The Regents of the University of California, 1951. - [https://projecteuclid.org/ euclid.bsmsp/1200500224](https://projecteuclid.org/ euclid.bsmsp/1200500224)

Document(s)

https://www.cirm-math.fr/RepOrga/2146/Slides/Yekutieli.pdf

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