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UID:2868@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20190408T140000
DTEND;TZID=Europe/Paris:20190408T150000
DTSTAMP:20190324T130000Z
URL:https://www.i2m.univ-amu.fr/evenements/estimation-et-clustering-dans-u
 n-modele-a-blocs-stochastiques-pour-des-reseaux-d-interaction-longitudinau
 x/
SUMMARY: (...): Estimation et clustering dans un modèle à blocs stochasti
 ques pour des réseaux d'interaction longitudinaux
DESCRIPTION:: In this work\, we introduce a Poisson process stochastic bloc
 k model for recurrent interaction events\, where each individual belongs t
 o a latent group and interactions between two individuals follow a conditi
 onal inhomogeneous Poisson process whose intensity is driven by the indivi
 duals' latent groups. The model is semiparametric as the  intensities per 
 group pair are modeled in a nonparametric way.We propose an estimation pro
 cedure\, relying on a semiparametric version of a variational expectation-
 maximization algorithm. Two different versions of the method are proposed\
 , using either histogram-type (with an adaptive choice of the partition si
 ze) or kernel intensity estimators. We also propose an integrated classifi
 cation likelihood criterion to select the number of latent groups. We carr
 y out synthetic experiments and analyse different real datasets to illustr
 ate our approach.This is joint work with Catherine Matias and Tabea Rebafk
 a.https://www.lpsm.paris/pageperso/villers/
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DTSTART:20190331T030000
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