Bayesian Adaptive Selection Under Prior Ignorance

Tathagata Basu, Matthias C. M. Troffaes, Jochen Einbeck

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


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.
Original languageEnglish
Title of host publicationInternational Conference on Uncertainty Quantification Optimisation 2020
Place of PublicationCham
Number of pages14
ISBN (Electronic)9783030805425
ISBN (Print)9783030805418
Publication statusPublished - 16 Jul 2021
Event UQOP: International Conference on Uncertainty Quantification & Optimisation - Virtual Event
Duration: 16 Nov 202019 Nov 2020

Publication series

NameSpace Technology Proceedings
ISSN (Print)1389-1766


Conference UQOP: International Conference on Uncertainty Quantification & Optimisation
Abbreviated titleUQOP 2020


  • Bayesian variable selection
  • statistics
  • high dimensional statistics
  • sensitivity analysis
  • variable selection
  • imprecise probability


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