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Gaussian Process Minima Analysis Unveiled in New Research

By PulseAugur Editorial · [1 sources] ·

This paper investigates the high minima of Gaussian processes, focusing on overshoots and the locations of minimizers. It demonstrates that under certain conditions, the scaled overshoot converges to an exponential random variable as the minimum value increases. The research also shows that weak subsequential limits of conditional laws for minimizers correspond to optimal covariance-energy measures, with convergence occurring if this measure is unique. These findings are illustrated using examples such as stationary Gaussian processes, fractional Brownian motion, and fractional Brownian sheets. AI

IMPACT Provides theoretical underpinnings for understanding complex data distributions in machine learning.

RANK_REASON Academic paper on a specific statistical modeling technique. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gaussian Process Minima Analysis Unveiled in New Research

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Academic paper on a specific statistical modeling technique. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Enkelejd Hashorva, Svyatoslav Novikov ·

    High Minima of Gaussian Processes: Overshoots and Minimizer Locations

    arXiv:2607.20714v1 Announce Type: cross Abstract: Let $X(t)$, $t\in K$, be a centred Gaussian process with continuous sample paths on a compact metric space $K$, and let $M=\min_{t\in K}X(t)$. Let $\sigma_*^2$ denote the minimum covariance energy associated with $X$, and assume t…

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