A Geometric Approach to Lower Bounds for the Maximum of Gaussian Random Processes

The paper proposes an approach to lower bounds for the expectation of the maximum of a Gaussian process based on classical results from the geometry of convex bodies.

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Publication Details

Journal
Mathematical Notes
Published
2024-12-01
DOI
https://doi.org/10.1134/s0001434624110361
Primary Topic
Gaussian Processes and Bayesian Inference
Type
article
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article

A Geometric Approach to Lower Bounds for the Maximum of Gaussian Random Processes

Борис Сергеевич Кашин
Mathematical Notes
Gaussian Processes and Bayesian Inference
article

A Geometric Approach to Lower Bounds for the Maximum of Gaussian Random Processes

Борис Сергеевич Кашин
article en

Abstract

The paper proposes an approach to lower bounds for the expectation of the maximum of a Gaussian process based on classical results from the geometry of convex bodies.

Mathematical NotesVol. 116(5-6)
Russian Academy of Sciences (RU), Lomonosov Moscow State University (RU), Steklov Mathematical Institute (RU)
Openalex Percentile: Top 75%
Gaussian Processes and Bayesian Inference
DOISourceOpenAlex
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A Geometric Approach to Lower Bounds for the Maximum of Gaussian Random Processes — Борис Сергеевич Кашин · Mathematical Notes (2024) | TGRS Research Map | TGRS