A study of Jeffrey’s rule with imprecise probability models

Enrique Miranda, Arthur Van Camp
Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 215:344-355, 2023.

Abstract

Jeffrey’s rule tells us how to update our beliefs about a probability measure when we have updated information conditional on some partition of the possibility space, while keeping the original marginal information on this partition. It is linked to the law of total probability, and is therefore connected to the notion of marginal extension of coherent lower previsions. In this paper, we investigate its formulation for some other imprecise probability models that are either more general (choice functions) or more particular (possibility measures, distortion models) than coherent lower previsions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v215-miranda23c, title = {A study of {J}effrey’s rule with imprecise probability models}, author = {Miranda, Enrique and Van Camp, Arthur}, booktitle = {Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications}, pages = {344--355}, year = {2023}, editor = {Miranda, Enrique and Montes, Ignacio and Quaeghebeur, Erik and Vantaggi, Barbara}, volume = {215}, series = {Proceedings of Machine Learning Research}, month = {11--14 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v215/miranda23c/miranda23c.pdf}, url = {https://proceedings.mlr.press/v215/miranda23c.html}, abstract = {Jeffrey’s rule tells us how to update our beliefs about a probability measure when we have updated information conditional on some partition of the possibility space, while keeping the original marginal information on this partition. It is linked to the law of total probability, and is therefore connected to the notion of marginal extension of coherent lower previsions. In this paper, we investigate its formulation for some other imprecise probability models that are either more general (choice functions) or more particular (possibility measures, distortion models) than coherent lower previsions.} }
Endnote
%0 Conference Paper %T A study of Jeffrey’s rule with imprecise probability models %A Enrique Miranda %A Arthur Van Camp %B Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications %C Proceedings of Machine Learning Research %D 2023 %E Enrique Miranda %E Ignacio Montes %E Erik Quaeghebeur %E Barbara Vantaggi %F pmlr-v215-miranda23c %I PMLR %P 344--355 %U https://proceedings.mlr.press/v215/miranda23c.html %V 215 %X Jeffrey’s rule tells us how to update our beliefs about a probability measure when we have updated information conditional on some partition of the possibility space, while keeping the original marginal information on this partition. It is linked to the law of total probability, and is therefore connected to the notion of marginal extension of coherent lower previsions. In this paper, we investigate its formulation for some other imprecise probability models that are either more general (choice functions) or more particular (possibility measures, distortion models) than coherent lower previsions.
APA
Miranda, E. & Van Camp, A.. (2023). A study of Jeffrey’s rule with imprecise probability models. Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 215:344-355 Available from https://proceedings.mlr.press/v215/miranda23c.html.

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