[ibayesclub.beta] Bayes club: 19th of March, from 2pm

[Botond Szabó]

Dear All,

The talk of Ankur Moitra (MIT) starts in 5 minutes. (In my earlier email it
was a bit hidden that the starting times are given in CET).

Best,
Botond

Szabo, B.T. <b.t.szabo at vu.nl> ezt írta (időpont: 2021. márc. 18., Cs,
11:02):

> Dear All,
>
> This is a kind reminder that the next meeting of the (International) Bayes
>  Club (https://www.math.vu.nl/thebayesclub/) will take place tomorrow,
> (Friday, the 19th of March*) *from 2 pm. We have two speakers: Joris
> Bierkens (Delft) and Ankur Moitra (MIT). This is a special occasion as we
> celebrate our 10th year anniversary therefore we plan to meet afterwards
> (from around 4-4:15 pm) at:
>
> https://gather.town/app/4lK8Zj28Ys0WtutP/IBC
> Password: bayesbayesbayes
>
> Please don't forget to
> prepare with beers/wine/non alcoholic beverages so we can toast virtually.
> Would be great if everyone could arrange to stay for a while after the
> talks as well. Please find below the titles and the abstracts. The
> seminar will be on zoom:
>
> -------------------------------------------------
>
> Topic: International Bayes club
> Time: Mar 19, 2021 02:00 PM Amsterdam
>
> Join Zoom Meeting
> https://vu-live.zoom.us/j/91629816385?pwd=T1RKLzRxTWw4N2hOcXYrb2ppMUh0UT09
>
> Meeting ID: 916 2981 6385
> Passcode: 904232
>
>
> --------------------------------------
> (around) 2pm lecture by Joris Bierkens (TU Delft)
>
> Title: The Boomerang Sampler and Bayesian inference for diffusions
>
> Abstract: The Bayesian estimation of (parameters of) a diffusion process
> is
> challenging due to the lack of a closed form expression for the
> transition densities. A natural approach is employing a Gibbs sampler in
> which the diffusion process has to be simulated for a fixed choice of
> parameters as a latent variable.
>
> This simulation of a specified diffusion is challenging itself due to
> its infinite dimensional and nonlinear nature. We discuss a recent
> approach to this problem involving the Boomerang Sampler, which is an
> example of a piecewise deterministic Monte Carlo sampler. The Boomerang
> Sampler has important differences as well as similarities with other
> piecewise deterministic samplers such as the Zig-Zag process and the
> Bouncy Particle Sampler.
>
> This talk is based upon joint work with Sebastiano Grazzi, Kengo
> Kamatani, Gareth Roberts, Moritz Schauer and Frank van der Meulen.
>
> ----------------
> (around) 3pm lecture by Ankur Moitra (MIT)
>
> Title: An Invitation to Semi-Random Models for Bayesian Statisticians
>
> Abstract: The stochastic block model is one of the oldest and most
> ubiquitous models for studying clustering and community detection. The
> basic inference problem is to use the observed graph to estimated the
> planted community structure that was used to generate it. Exciting recent
> work has precisely characterized when it is possible to achieve non-trivial
> accuracy, and what the optimal error behaves like. These works all revolve
> around belief propagation which attempts to sample from the posterior
> distribution using a simple iterative algorithm.
>
> Here we revisit these thresholds from the perspective of semirandom models
> where we allow an adversary to make `helpful’ changes that strengthen ties
> within each community and break ties between them. We show a surprising
> result that these `helpful’ changes can shift the information-theoretic
> threshold, making the community detection problem strictly harder.
> Conceptually, any algorithm meeting the exact information-theoretic
> threshold in the average-case model must exploit the precise structure of
> the noise. These results point to an interesting new direction: Can
> semirandom (and related) models help explain why some algorithms are
> preferred to others in practice, in spite of gaps in their average-case
> performance?
>
> This is based on joint work with Amelia Perry and Alex Wein.
>
> Best wishes,
> Botond (on behalf of the organising team)
>
>
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