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Bayesian Adaptive Selection Under Prior Ignorance

Basu, Tathagata; Troffaes, Matthias C.M.; Einbeck, Jochen; Vasile, Massimiliano; Quagliarella, Domenico

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Tathagata Basu

Massimiliano Vasile

Domenico Quagliarella


Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have been proposed for variable selection. However, a convincing robust Bayesian approach is yet to be investigated. Here in this work, we investigate sensitivity analysis over a simplex of probability measures. We sample from this simplex to get an inclusion probability of each variable. The sensitivity analysis gives us a set of posteriors instead of a single posterior. This set of posteriors gives us a behaviour of the model parameters with respect to different prior elicitations resulting in robust inferential conclusions.


Basu, T., Troffaes, M. C., Einbeck, J., Vasile, M., & Quagliarella, D. (2021). Bayesian Adaptive Selection Under Prior Ignorance. .

Conference Name UQOP 2020
Online Publication Date Jul 16, 2021
Publication Date 2021
Deposit Date Feb 7, 2022
Publicly Available Date Jul 16, 2022
Volume 8
Pages 365-378
Series Title Space Technology Proceedings
Series ISSN 1389-1766
ISBN 978-3-030-80541-8


Accepted Conference Proceeding (289 Kb)

Copyright Statement
This a post-peer-review, pre-copyedit version of a chapter published in Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. The final authenticated version is available online at:

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