Processing Multiple Distortion Models: a Comparative Study

Sébastien Destercke, Ignacio Montes, Enrique Miranda
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:122-131, 2021.

Abstract

When dealing with uncertain information, distortion or neighbourhood models are convenient practical tools, as they rely on very few parameters. In this paper, we study their behaviour when such models are combined and processed. More specifically, we study their behaviour when merging different distortion models quantifying uncertainty on the same quantity, and when manipulating distortion models defined over multiple variables.

Cite this Paper

BibTeX
@InProceedings{pmlr-v147-destercke21a, title = {Processing Multiple Distortion Models: a Comparative Study}, author = {Destercke, S\'ebastien and Montes, Ignacio and Miranda, Enrique}, booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications}, pages = {122--131}, year = {2021}, editor = {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n}, volume = {147}, series = {Proceedings of Machine Learning Research}, month = {06--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v147/destercke21a/destercke21a.pdf}, url = {https://proceedings.mlr.press/v147/destercke21a.html}, abstract = {When dealing with uncertain information, distortion or neighbourhood models are convenient practical tools, as they rely on very few parameters. In this paper, we study their behaviour when such models are combined and processed. More specifically, we study their behaviour when merging different distortion models quantifying uncertainty on the same quantity, and when manipulating distortion models defined over multiple variables.} }
Endnote
%0 Conference Paper %T Processing Multiple Distortion Models: a Comparative Study %A Sébastien Destercke %A Ignacio Montes %A Enrique Miranda %B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2021 %E Andrés Cano %E Jasper De Bock %E Enrique Miranda %E Serafı́n Moral %F pmlr-v147-destercke21a %I PMLR %P 122--131 %U https://proceedings.mlr.press/v147/destercke21a.html %V 147 %X When dealing with uncertain information, distortion or neighbourhood models are convenient practical tools, as they rely on very few parameters. In this paper, we study their behaviour when such models are combined and processed. More specifically, we study their behaviour when merging different distortion models quantifying uncertainty on the same quantity, and when manipulating distortion models defined over multiple variables.
APA
Destercke, S., Montes, I. & Miranda, E.. (2021). Processing Multiple Distortion Models: a Comparative Study. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:122-131 Available from https://proceedings.mlr.press/v147/destercke21a.html.

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