Seminar Series: Jean-Francois Couerjolly

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Jean-Francois Coeurjolly
January 14, 2021
3:00PM - 4:00PM
Location
Virtual Event

Date Range
Add to Calendar 2021-01-14 15:00:00 2021-01-14 16:00:00 Seminar Series: Jean-Francois Couerjolly Title Repulsiveness for integration (not my social program) Presentation Link Speaker Jean-Francois Coeurjolly, University Grenoble Alpes - France Abstract Integral estimation in any dimension is an extensive topic, largely treated in the literature, with a broad range of applications. Monte-Carlo type methods arise naturally when one looks forward to quantifying/controlling the error. Many methods have already been developped: MCMC, Poisson disk sampling, QMC (and randomized versions), Bayesian quadrature, etc. In this talk, I’ll consider a different approach which consists in defining the quadrature nodes as the realization of a spatial point process. In particular I’ll show that a very specific class of determinantal point processes, a class of repulsive point patterns, has excellent properties and is able to estimate efficiently integrals for non-differentiable functions with an explicit and faster rate of convergence than current methods. Virtual Event Department of Statistics webmaster@stat.osu.edu America/New_York public
Description

Title

Repulsiveness for integration (not my social program)

Presentation Link

Speaker

Jean-Francois Coeurjolly, University Grenoble Alpes - France

Abstract

Integral estimation in any dimension is an extensive topic, largely treated in the literature, with a broad range of applications. Monte-Carlo type methods arise naturally when one looks forward to quantifying/controlling the error. Many methods have already been developped: MCMC, Poisson disk sampling, QMC (and randomized versions), Bayesian quadrature, etc. In this talk, I’ll consider a different approach which consists in defining the quadrature nodes as the realization of a spatial point process. In particular I’ll show that a very specific class of determinantal point processes, a class of repulsive point patterns, has excellent properties and is able to estimate efficiently integrals for non-differentiable functions with an explicit and faster rate of convergence than current methods.