Professor Matthias Troffaes matthias.troffaes@durham.ac.uk
Professor
A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability
Troffaes, Matthias C.M.; Basu, Tathagata
Authors
Tathagata Basu
Contributors
Jasper De Bock
Editor
Cassio P. de Campos
Editor
Gert de Cooman
Editor
Erik Quaeghebeur
Editor
Gregory Wheeler
Editor
Abstract
In this paper we prove a new probability inequality that can be used to construct p-boxes in a non-parametric fashion, using the sample mean and sample standard deviation instead of the true mean and true standard deviation. The inequality relies only on exchangeability and boundedness.
Citation
Troffaes, M. C., & Basu, T. (2019). A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability. In J. D. Bock, C. P. . D. Campos, G. D. Cooman, E. Quaeghebeur, & G. Wheeler (Eds.), Proceedings of the Eleventh International Symposium on Imprecise Probabilities : Theories and Applications (386-393)
Conference Name | ISIPTA'19 |
---|---|
Conference Location | Ghent |
Acceptance Date | Apr 26, 2019 |
Publication Date | 2019 |
Deposit Date | Jun 17, 2019 |
Publicly Available Date | Jun 18, 2019 |
Pages | 386-393 |
Series Title | Proceedings of machine learning research |
Series Number | 103 |
Series ISSN | 2640-3498 |
Book Title | Proceedings of the Eleventh International Symposium on Imprecise Probabilities : Theories and Applications. |
Public URL | https://durham-repository.worktribe.com/output/1142573 |
Publisher URL | http://proceedings.mlr.press/v103/troffaes19a.html |
Files
Accepted Conference Proceeding
(191 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This paper has been published under a Creative Commons Attribution 4.0 International License specified at http://creativecommons.org/licenses/by/4.0/legalcode (human readable summary at http://creativecommons.org/licenses/by/4.0).
Published Conference Proceeding
(191 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Uncertainty Quantification in Lasso-Type Regularization Problems
(2020)
Book Chapter
A sensitivity analysis of adaptive lasso
(2019)
Presentation / Conference
Robust uncertainty quantification for measurement problems with limited information
(2019)
Presentation / Conference
Binary Credal Classification Under Sparsity Constraints
(2020)
Conference Proceeding
Bayesian Adaptive Selection Under Prior Ignorance
(2021)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search