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Hello all! This is the thread of discussion for the talk of Tobias Fritz, Tomáš Gonda, Paolo Perrone and Eigil Rischel, "Distribution functors, second-order stochastic dominance and the Blackwell-Sherman-Stein Theorem in Categorical Probability".
Date and time: Monday July 6, 16:00 UTC.
Zoom meeting: https://mit.zoom.us/j/7055345747
YouTube live stream: https://www.youtube.com/watch?v=1WhsWK20iRo&list=PLCOXjXDLt3pZDHGYOIqtg1m1lLOURjl1Q
For more background on Markov categories, probability monads, and categorical probability in general, this is the playlist you are looking for: https://www.youtube.com/playlist?list=PLaILTSnVfqtIebAXFOcee9MvAyBwhIMyr
In this talk, I will be speaking about a project tentatively titled:
It is a work in collaboration with Tobias Fritz, Paolo Perrone, and Eigil Rischel; in which we study what can one do with Markov categories that have distribution objects, which one can think of as spaces of measures. With this kind of structure, we can prove abstract versions of results such as:
The equivalence between the existence of partial evaluations between two measures and the second-order stochastic dominance relation of the two measures. The latter is a common way to compare measures in terms of how "spread out" they are and the former is the lowest level of the bar construction.
Blackwell-Sherman-Stein Theorem. This result characterizes the "informativeness" order relation of statistical models defined in terms of the existence of a simulation of one from the other. In particular, it shows that this relation is isomorphic to the second-order stochastic dominance relation of measures over the space of posterior distributions over the tested hypotheses that a Bayesian agent would assign having performed the statistical experiment. That is, a statistical experiment is more informative iff the posteriors are more spread out (so that doing the experiment allows one to distinguish between the hypotheses better), in a precise sense that we can now make use of in any suitable Markov category.
Apart from generalizing some known results from mathematical statistics, we also advance the theoretical understanding of Markov categories relevant to these concepts. Specifically, we elucidate the relation between Markov categories and monoidal monads - showing under what conditions can we think of a Markov category as the Kleisli category of a probability monad (and vice versa).
And to complement Paolo's link to the talks from a recent conference on categorical probability, here's a nice playlist of introductory videos on the topic by @Arthur Parzygnat.
Thanks for subscribing me, @Tomáš Gonda! This looks really cool!
Here are my slides for the presentation :slight_smile:
We start in 5 minutes!
The paper by Cho and Jacobs: https://arxiv.org/abs/1709.00322
Here's the video!
https://www.youtube.com/watch?v=fgWUV-hE0CI&list=PLCOXjXDLt3pYot9VNdLlZqGajHyZUywdI
By the way, for the people interested in probability and statistics, you may want to take a look at the stream of the categorical probability conference.
We also have a dedicated Jitsi room,
Probability - random topics are welcome and encouraged.
Hello! If anyone is interested, this week Tobias Fritz will give a talk about Markov categories at the MIT Categories Seminar.
Thread here: https://categorytheory.zulipchat.com/#narrow/stream/229457-MIT-Categories.20Seminar/topic/July.2016.3A.20Tobias.20Fritz'.20talk
This project is now on arXiv with quite a bit of new content added since July: https://arxiv.org/abs/2010.07416
Hello all! There's a talk on Markov category here, this week: https://categorytheory.zulipchat.com/#narrow/stream/229457-MIT-Categories.20Seminar/topic/November.2019.3A.20Eigil.20Rischel's.20talk