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Part 2: Foundations of Neo-Bayesian Statistics(s)


20 mars 2023   11h15 — 12h15

Massimiliano Amarante Université de Montréal, Canada

Massimiliano  Amarante

Présentation sur YouTube.
Pour assister à cette conférence, il est proposé de participer à la partie 1 à 10h.

We will take off from the observation that Savage’s axioms (or any axiomatization of EU) map Statistics into (classical) probability theory. Consequently, the axioms of any non-Bayesian model must map Statistics into something else. This observation raises the question of which Statistical theories would emerge via this mapping. We show that many non-Bayesian models map Statistics into alternative theories of probability that display the same structure as classical probability and differ from the latter only in the notion of approximation they use. We then build a general framework encompassing all the models displaying this structure and use it to identify the rules of inference corresponding to several popular models. We conclude by discussing the implications of our findings for issues such dynamic consistency, updating of capacities as well as some open problems.

Federico Bobbio responsable
Léa Ricard responsable
Defeng Liu responsable


Séminaire hybride au GERAD
Zoom et salle 4488
Pavillon André-Aisenstadt
Campus de l'Université de Montréal
2920, chemin de la Tour

Montréal Québec H3T 1J4

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