[Forum SIS] Webinar in Statistics ANDREA OTTOLINI - 19/3, ore 17

[Pierpaolo De Blasi]

WEBINARS IN STATISTICS @ COLLEGIO CARLO ALBERTO
<https://www.carloalberto.org/events/category/seminars/seminars-in-statistics/?tribe-bar-date=2019-09-01>

Venerdi 19 Marzo 2021, alle ore 17:00, si terrà  il seguente webinar:

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Speaker: *Andrea Ottolini (*Standford University, USA)


Title: *Gibbs sampling in the analysis of priors for almost exchangeable
data*


Zoom link:

https://us02web.zoom.us/j/83130864007?pwd=Z2dGbHVsWHJMMG9iaTJFb2VMcExqQT09

Meeting ID: 831 3086 4007

Passcode: 222589

Abstract:
Consider a population of N individuals divided into d subgroups (e.g., d=4
and people are divided by sex and smoking habits). A sequence of 0-1 valued
experiments on the population with outcomes X_1,..., X_n is called
partially exchangeable if the only relevant information in the data is the
number of 1's in each category. de Finetti's representation result
guarantees that the distribution of the X's (for n<<N) is in bijection with
measures pi^{(n)} on [0,1]^d. Natural problems -- such as understanding
which of the subdivisions are truly meaningful and what is the effect of
our initial belief pi^{(0)} -- can be addressed if one knows how to sample
efficiently from these measures. In the absence of further information
about the experiments, de Finetti suggests starting with a class of priors
pi^{(0)}_{C,A} indexed by a d-dimensional network (measuring the relative
differences among categories) and a positive parameter A (measuring the
belief in complete exchangeability). in this talk I will analyze the
performance of Gibbs sampling in approximating these measures in the almost
exchangeable regime (A>>1). It will be shown that A^2 steps are necessary
and sufficient to mix in a certain Wasserstein distance, with constants
depending on few spectral parameters of the network C. This is based on
joint work with Gerencsér.

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Il webinar è organizzato dalla "de Castro" Statistics Initiative

www.carloalberto.org/stats

in collaborazione con il Collegio Carlo Alberto e rientra nel Complex Data
Modeling Research Network

midas.mat.uc.cl/network


Cordiali saluti,

Pierpaolo De Blasi

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University of Torino & Collegio Carlo Alberto

carloalberto.org/pdeblasi
<https://sites.google.com/a/carloalberto.org/pdeblasi/>
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