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Title:聽Repulsiveness for integration (not my social program)

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鈥檒l consider a different approach which consists in defining the quadrature nodes as the realization of a spatial point process. In particular I鈥檒l 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.

Jean-Francois Coeurjolly is Professor of Statistics in the Department of Mathematics, UQAM. His research focuses on random fields, fractional processes, spatial statistics. He serves as the associate editor for Scandinavian Journal of Statistics and Statistics and Probability Letters.