Aggregating Belief Models

Seamus Bradley
Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:38-48, 2019.

Abstract

This paper has two goals. The first goal is to say something about how one might combine different agents’ imprecise probabilities to generate an aggregate imprecise probability. The second goal is to champion the very general theory of “belief models” (de Cooman “Belief models: an order theoretic investigation” Annals of Mathematics and AI 2005) which, I think, deserves more attention. The belief models framework is interesting partly because many other formal models of reasoning appear as special cases of belief models (for example, propositional logic, ranking functions, imprecise probability).

Cite this Paper

BibTeX
@InProceedings{pmlr-v103-bradley19a, title = {Aggregating Belief Models}, author = {Bradley, Seamus}, booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {38--48}, year = {2019}, editor = {De Bock, Jasper and de Campos, Cassio P. and de Cooman, Gert and Quaeghebeur, Erik and Wheeler, Gregory}, volume = {103}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v103/bradley19a/bradley19a.pdf}, url = {https://proceedings.mlr.press/v103/bradley19a.html}, abstract = {This paper has two goals. The first goal is to say something about how one might combine different agents’ imprecise probabilities to generate an aggregate imprecise probability. The second goal is to champion the very general theory of “belief models” (de Cooman “Belief models: an order theoretic investigation” Annals of Mathematics and AI 2005) which, I think, deserves more attention. The belief models framework is interesting partly because many other formal models of reasoning appear as special cases of belief models (for example, propositional logic, ranking functions, imprecise probability).} }
Endnote
%0 Conference Paper %T Aggregating Belief Models %A Seamus Bradley %B Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2019 %E Jasper De Bock %E Cassio P. de Campos %E Gert de Cooman %E Erik Quaeghebeur %E Gregory Wheeler %F pmlr-v103-bradley19a %I PMLR %P 38--48 %U https://proceedings.mlr.press/v103/bradley19a.html %V 103 %X This paper has two goals. The first goal is to say something about how one might combine different agents’ imprecise probabilities to generate an aggregate imprecise probability. The second goal is to champion the very general theory of “belief models” (de Cooman “Belief models: an order theoretic investigation” Annals of Mathematics and AI 2005) which, I think, deserves more attention. The belief models framework is interesting partly because many other formal models of reasoning appear as special cases of belief models (for example, propositional logic, ranking functions, imprecise probability).
APA
Bradley, S.. (2019). Aggregating Belief Models. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 103:38-48 Available from https://proceedings.mlr.press/v103/bradley19a.html.

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