Monte Carlo integration of non-differentiable functions on \([0,1]^\iota\), \(\iota =1,\ldots, d\), using a single determinantal point pattern defined on \([0,1]^d\)

DOI10.1214/21-EJS1929zbMath1483.60072arXiv2003.10323MaRDI QIDQ2074323

Adrien Mazoyer, Jean-François Coeurjolly, Pierre-Olivier Amblard

Publication date: 9 February 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.10323

zbMATH Keywords

numerical integrationdeterminantal point processesoptimal designs

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Design of statistical experiments (62K99) Monte Carlo methods (65C05) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (3)

On simulation of continuous determinantal point processes ⋮ Extended L-ensembles: a new representation for determinantal point processes ⋮ On estimating the structure factor of a point process, with applications to hyperuniformity

Uses Software

• spatstat
• rhalton
• GitHub
• DPPy
• Algorithm 247

Cites Work

• Local antithetic sampling with scrambled nets
• Determinantal processes and independence
• Scrambled net variance for integrals of smooth functions
• Random point fields associated with certain Fredholm determinants. I: Fermion, Poisson and Boson point processes.
• The central limit theorem for local linear statistics in classical compact groups and related combinatorial identities
• Gaussian limit for determinantal random point fields.
• Monte Carlo with determinantal point processes
• Asymptotic normality of scrambled geometric net quadrature
• Determinantal point process models on the sphere
• Determinantal random point fields
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• An analysis of variance test for normality (complete samples)
• Determinantal Point Process Models and Statistical Inference
• High-dimensional integration: The quasi-Monte Carlo way
• Statistical Analysis and Modelling of Spatial Point Patterns
• A Modified Monte-Carlo Quadrature
• On the distribution of points in a cube and the approximate evaluation of integrals
• Determinantal Point Processes
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