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February 2020 Monte Carlo with determinantal point processes
Rémi Bardenet, Adrien Hardy
Ann. Appl. Probab. 30(1): 368-417 (February 2020). DOI: 10.1214/19-AAP1504


We show that repulsive random variables can yield Monte Carlo methods with faster convergence rates than the typical $N^{-1/2}$, where $N$ is the number of integrand evaluations. More precisely, we propose stochastic numerical quadratures involving determinantal point processes associated with multivariate orthogonal polynomials, and we obtain root mean square errors that decrease as $N^{-(1+1/d)/2}$, where $d$ is the dimension of the ambient space. First, we prove a central limit theorem (CLT) for the linear statistics of a class of determinantal point processes, when the reference measure is a product measure supported on a hypercube, which satisfies the Nevai-class regularity condition; a result which may be of independent interest. Next, we introduce a Monte Carlo method based on these determinantal point processes, and prove a CLT with explicit limiting variance for the quadrature error, when the reference measure satisfies a stronger regularity condition. As a corollary, by taking a specific reference measure and using a construction similar to importance sampling, we obtain a general Monte Carlo method, which applies to any measure with continuously derivable density. Loosely speaking, our method can be interpreted as a stochastic counterpart to Gaussian quadrature, which at the price of some convergence rate, is easily generalizable to any dimension and has a more explicit error term.


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Rémi Bardenet. Adrien Hardy. "Monte Carlo with determinantal point processes." Ann. Appl. Probab. 30 (1) 368 - 417, February 2020.


Received: 1 March 2017; Revised: 1 November 2018; Published: February 2020
First available in Project Euclid: 25 February 2020

zbMATH: 07200531
MathSciNet: MR4068314
Digital Object Identifier: 10.1214/19-AAP1504

Primary: 65C05
Secondary: 42C05 , 60F05

Keywords: central limit theorems , Determinantal point processes , hyperuniformity , Monte Carlo methods , orthogonal polynomials

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.30 • No. 1 • February 2020
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