The Society for Imprecise Probabilities:
Theories and Applications

SIPTA Seminars

Are you a curious student that has just started to explore the topic of imprecise probabilities? Or an experienced researcher that would like to keep in touch with the community? Join us at the SIPTA Seminars, an online series of seminars on imprecise probabilities (IP). The seminars are open to anyone interested in IP, and are followed by a Q&A and open discussion. They take place roughly once per month, with a break over the summer. Topics range from foundational IP theories to applications that can benefit from IP approaches.

Details about the individual seminars are available in the list below. Close to the date of the next seminar, a zoom link will be provided there as well, which is freely accessible. If you click it, you will first be taken to a waiting room; please be patient until the organizers let you in. During the talk, questions should be put in the chat, and the audience is expected to mute their microphones. After the talk, there will be time for Q&A and discussion, at which point you can turn on your microphone when you want to contribute. The talk (but not the Q&A and discussion) will be recorded, and will afterwards be made freely available on the SIPTA Youtube channel.

The organisation is taken care of by Sébastien Destercke, Enrique Miranda and Jasper De Bock. If you have questions about the seminars, or suggestions for future speakers, you can get in touch with us at Suggestions for prominent speakers outside the IP community, whose work is nevertheless related to IP, are especially welcome.

Upcoming Seminars

E is the new P

Peter Grünwald 29 September 2022, 15:00 CEST

Zoom link:
We develop a representation of a decision maker's uncertainty based on e-values, a recently proposed alternative to the p-value. Like the Bayesian posterior, this e-posterior allows for making predictions against arbitrary loss functions that do not have to be specified ex ante. Unlike the Bayesian posterior, it provides risk bounds that have frequentist validity irrespective of prior adequacy: if the e-collection (which plays a role analogous to the Bayesian prior) is chosen badly, bounds get loose rather than wrong. As a consequence, e-posterior minimax decision rules are safer than Bayesian ones. The resulting quasi-conditional paradigm addresses foundational issues in statistical inference. If the losses under consideration have a special property which we call Condition Zero, risk bounds based on the standard e-posterior are equivalent to risk bounds based on a `capped' version of it. We conjecture that this capped version can be interpreted in terms of possibility measures and Martin-Liu inferential models.


Fabio Cozman 29 November 2022, 15:00 CEST

The Zoom link will appear here close to the start of the seminar

Past Seminars

Imprecise probabilities in modern data science: challenges and opportunities

Ruobin Gong 29 June 2022, 15:00 CEST

Download the slides Watch on YouTube
Imprecise probabilities (IP) capture structural uncertainty intrinsic to statistical models. They offer a richer vocabulary with which the modeler may articulate specifications without concocting unwarranted assumptions. While IP promises a principled approach to data-driven decision making, its use in practice has so far been limited. Two challenges to its popularization are 1) IP reasoning may defy the intuition we derive from precise probability models, and 2) IP models may be difficult to compute. On the other hand, recent developments in formal privacy present a unique opportunity for IP to contribute to responsible data dissemination. Case in point is differential privacy (DP), a cryptographically motivated framework endorsed by corporations and official statistical agencies including the U.S. Census Bureau. I discuss how IP offers the correct language for DP, both descriptive and inferential, particularly when the privacy mechanism lacks transparency. These challenges and opportunities highlight the urgency to adapt IP research to meet the demands of modern data science.

Imprecision, not as a problem, but as part of the solution

Gert de Cooman 30 May 2022, 15:00 CEST

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Imprecision in probability theory is often considered to be unfortunate, something to be tolerated, and then only if there is no other way out. In this talk, I will argue that imprecision also has strongly positive sides, and that it can allow us to look at, approach and deal with existing problems in novel ways. I will provide a number of examples to corroborate for this thesis, based on my research experience in a number of fields: inference and decision making, stochastic processes, algorithmic randomness, game-theoretic probability, functional analysis, ...