Tathagata Basu
Bayesian Adaptive Selection Under Prior Ignorance
Basu, Tathagata; Troffaes, Matthias C.M.; Einbeck, Jochen
Authors
Professor Matthias Troffaes matthias.troffaes@durham.ac.uk
Professor
Professor Jochen Einbeck jochen.einbeck@durham.ac.uk
Professor
Contributors
Massimiliano Vasile
Editor
Domenico Quagliarella
Editor
Abstract
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.
Citation
Basu, T., Troffaes, M. C., & Einbeck, J. (2021, December). Bayesian Adaptive Selection Under Prior Ignorance. Presented at UQOP 2020
Presentation Conference Type | Conference Paper (published) |
---|---|
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 |
DOI | https://doi.org/10.1007/978-3-030-80542-5_22 |
Public URL | https://durham-repository.worktribe.com/output/1138537 |
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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: https://doi.org/10.1007/978-3-030-80542-5_22
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