
arXiv: 1712.02344
We investigate the realizations of a random Gaussian field on a finite domain of ${\mathbb R}^d$ in the limit where a given linear functional of the field is large. We prove that if its variance is bounded, the field converges uniformly and almost surely to a non random profile depending only on the covariance and the considered linear functional of the field. This is a significant improvement of the weaker $L^2$-convergence in probability previously obtained in the case of conditioning on a large quadratic functional.
7 pages, LaTeX + AMS + elsarticle (included), submitted to Statist. Probab. Lett
60G15, 60F99 (Primary), 60G60, 60G70 (Secondary), 330, Statistical Mechanics (cond-mat.stat-mech), [PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph], Probability (math.PR), Gaussian processes, extreme theory, FOS: Physical sciences, Mathematical Physics (math-ph), Gaussian fields, 510, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Extreme value theory; extremal stochastic processes, Limit theorems in probability theory, concentration properties, FOS: Mathematics, Random fields, Mathematics - Probability, Condensed Matter - Statistical Mechanics, Mathematical Physics
60G15, 60F99 (Primary), 60G60, 60G70 (Secondary), 330, Statistical Mechanics (cond-mat.stat-mech), [PHYS.MPHY]Physics [physics]/Mathematical Physics [math-ph], Probability (math.PR), Gaussian processes, extreme theory, FOS: Physical sciences, Mathematical Physics (math-ph), Gaussian fields, 510, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], Extreme value theory; extremal stochastic processes, Limit theorems in probability theory, concentration properties, FOS: Mathematics, Random fields, Mathematics - Probability, Condensed Matter - Statistical Mechanics, Mathematical Physics
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