[Forum SIS] Seminario Manuele Leonelli - 4 Maggio - Ca' Foscari

Ilaria Prosdocimi prosdocimi.ilaria a gmail.com
Mer 28 Apr 2021 08:53:18 CEST


Cari colleghi e colleghe,

Con grande piacere, vi segnalo il prossimo seminario del gruppo Statistica
al DAIS, Ca' Foscari (tutti i futuri seminari sono elencati alla pagina del
gruppo https://www.unive.it/pag/16818):

Data: 4 Maggio 2021 - ore 14:00-15:00
Titolo: Untangling complex dependencies in categorical data using staged
trees
Relatore: Manuele Leonelli (IE University Madrid)

Il seminario si potrà seguire tramite la piattaforma Zoom:
https://unive.zoom.us/j/82776377762
Meeting ID: 827 7637 7762 - Passcode: SanMarco1

Abstract:
The dependence structure of a categorical random vector is often studied by
means of a probabilistic graphical model. The most commonly used model is
the so-called Bayesian network which provides an intuitive and efficient
framework to assess (causal) dependencies. One of the major drawbacks of
these models is that they can only explicitly represent symmetric
dependencies, which, in practice, may not give a complete description of
the data dependence structure. Staged trees are a flexible class of
graphical models which can explicitly represent and model a wide array of
non-symmetric dependence. In this talk, I will provide an overview of this
model class and their application to a wide array of datasets. I will also
discuss a number of ongoing developments for staged trees, including
efficient structural learning, causal discovery, manipulations of the
graphs and the new stagedtrees R package. The talk is based on joint work
with Gherardo Varando (University of Valencia), Federico Carli and Eva
Riccomagno (University of Genova).

Cordiali saluti

Ilaria Prosdocimi

----
Ilaria Prosdocimi
Assistant Professor in Statistics
Ca' Foscari University of Venice
Department of Environmental Sciences, Informatics and Statistic
prosdocimi.ilaria a gmail.com
ilaria.prosdocimi a unive.it
-------------- parte successiva --------------
Un allegato HTML è stato rimosso...
URL: <http://www.stat.unipg.it/pipermail/sis/attachments/20210428/52ec323b/attachment.html>


Maggiori informazioni sulla lista Sis