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Pierre Barbillon
Researcher at Université Paris-Saclay
Publications - 71
Citations - 646
Pierre Barbillon is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Stochastic block model & Computer science. The author has an hindex of 12, co-authored 65 publications receiving 485 citations. Previous affiliations of Pierre Barbillon include University of Paris-Sud & Institut national de la recherche agronomique.
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Patterns of plant and animal protein intake are strongly associated with cardiovascular mortality: the Adventist Health Study-2 cohort.
Marion Tharrey,Marion Tharrey,François Mariotti,Andrew Mashchak,Pierre Barbillon,Maud Delattre,Gary E. Fraser +6 more
TL;DR: In this paper, a Cox regression analysis was used to estimate multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and dietary components, which indicated that plant and animal proteins are intimately associated with specific large nutrient clusters that may explain part of their complex relation with cardiovascular health.
Journal ArticleDOI
Stochastic block models for multiplex networks: an application to a multilevel network of researchers
Pierre Barbillon,Pierre Barbillon,Pierre Barbillon,Sophie Donnet,Sophie Donnet,Sophie Donnet,Emmanuel Lazega,Avner Bar-Hen +7 more
TL;DR: This work extends stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship and shows strong interactions between these two kinds of connection and the groups that are obtained.
Journal ArticleDOI
Nonlinear methods for inverse statistical problems
TL;DR: In order to limit the number of (usually burdensome) physical model runs inside the inversion algorithm to a reasonable level, a nonlinear approximation methodology making use of Kriging and a stochastic EM algorithm is presented.
Posted Content
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker,Pierre Barbillon,Arindam Fadikar,Robert B. Gramacy,Radu Herbei,David Higdon,Jiangeng Huang,Leah R. Johnson,Pulong Ma,Anirban Mondal,Bianica Pires,Jerome Sacks,Vadim Sokolov +12 more
TL;DR: This review aims to bring a spotlight to the growing prevalence of stochastic computer models -- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians, and an emphasis on open questions of relevance to practitioners and statisticians.
Journal ArticleDOI
Variational Inference for Stochastic Block Models From Sampled Data
TL;DR: In this article, nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM) were dealt with, and sampling designs and recover missin...