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 firstname.lastname@example.org.
Suggestions for prominent speakers outside the IP community, whose work is nevertheless related to IP, are especially welcome.
Upcoming SeminarsArgumentation techniques have received significant attention in Artificial Intelligence, particularly since 1995, when Dung proposed his "argumentation frameworks" and showed that they unify many branches of knowledge representation.
Argumentation frameworks that deal with uncertainty have been explored since then; often, these frameworks rely on imprecise or indeterminate probabilities.
Indeed, probabilistic argumentation frameworks may be one of the most promising applications of imprecise probabilities in Artificial Intelligence.
This talk will review the main ideas behind argumentation frameworks and how they are often connected with imprecise probabilities.
Engineering and IP: what's going on?Edoardo Pattelli, Alice Cicirello & Matthias Faes
14 December 2022, 15:00 CET
The Zoom link will appear here close to the start of the seminar
Past SeminarsLower probabilities, defined as normalised and monotone set functions, constitute one of the basic models within Imprecise Probability theory.
One of their interpretations allows building a bridge with coalitional game theory: the possibility space is regarded as a set of players who must share a reward, events represent coalitions of players who collaborate in order to obtain a greater reward, and the lower probability of a coalition represents the minimum reward that this collaboration can guarantee.
This correspondence makes lower probabilities and coalitional games formally equivalent, being the notation, terminology and interpretation the only difference.
As an example, coherent lower probabilities are the same as exact games, the credal set of the lower probability is referred to as the core of the game,...
In this presentation I dig into this connection, paying special attention to game solutions and their interpretation as centroids of the credal set.
In addition, I show that if we move to the more general setting of lower previsions, it is possible to represent information about the coalitions and their rewards that cannot be captured by the standard coalitional game theory.
This shows that lower previsions constitute a more general framework than the classical theory of coalitional games.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.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 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, ...