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Bayesian Adaptive Selection Under Prior Ignorance

Basu, Tathagata and Troffaes, Matthias C. M. and Einbeck, Jochen (2021) 'Bayesian Adaptive Selection Under Prior Ignorance.', UQOP 2020.


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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Publisher 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:
Date accepted:No date available
Date deposited:28 March 2022
Date of first online publication:16 July 2021
Date first made open access:16 July 2022

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