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research article

A Covariance Formula For Topological Events Of Smooth Gaussian Fields

Beliaev, Dmitry
•
Muirhead, Stephen
•
Rivera, Alejandro  
November 1, 2020
Annals Of Probability

We derive a covariance formula for the class of 'topological events' of smooth Gaussian fields on manifolds; these are events that depend only on the topology of the level sets of the field, for example, (i) crossing events for level or excursion sets, (ii) events measurable with respect to the number of connected components of level or excursion sets of a given diffeomorphism class and (iii) persistence events. As an application of the covariance formula, we derive strong mixing bounds for topological events, as well as lower concentration inequalities for additive topological functionals (e.g., the number of connected components) of the level sets that satisfy a law of large numbers. The covariance formula also gives an alternate justification of the Harris criterion, which conjecturally describes the boundary of the percolation university class for level sets of stationary Gaussian fields. Our work is inspired by (Ann. Inst. Henri Poincare Probab. Stat. 55 (2019) 1679-1711), in which a correlation inequality was derived for certain topological events on the plane, as well as by (Asymptotic Methods in the Theory of Gaussian Processes and Fields (1996) Amer. Math. Soc.), in which a similar covariance formula was established for finite-dimensional Gaussian vectors.

  • Details
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Type
research article
DOI
10.1214/20-AOP1438
Web of Science ID

WOS:000581921000005

Author(s)
Beliaev, Dmitry
Muirhead, Stephen
Rivera, Alejandro  
Date Issued

2020-11-01

Publisher

INST MATHEMATICAL STATISTICS

Published in
Annals Of Probability
Volume

48

Issue

6

Start page

2845

End page

2893

Subjects

Statistics & Probability

•

Mathematics

•

gaussian fields

•

topology

•

covariance formula

•

central-limit-theorem

•

percolation

•

sets

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
RGM  
Available on Infoscience
November 24, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/173553
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