Gradient-type estimates for the dynamic φ24-model

IF 1.1 2区 数学 Q3 STATISTICS & PROBABILITY
Florian Kunick , Pavlos Tsatsoulis
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引用次数: 0

Abstract

We prove gradient bounds for the Markov semigroup of the dynamic φ24-model on a torus of fixed size L>0. For sufficiently large mass m>0 these estimates imply exponential contraction of the Markov semigroup. Our method is based on pathwise estimates of the linearized equation. To compensate the lack of exponential integrability of the stochastic drivers we use a stopping time argument in the spirit of Cass–Litterer–Lyons (Cass et al., 2013) and the strong Markov property. Following the classical approach of Bakry-Émery, as a corollary we prove a Poincaré/spectral gap inequality for the φ24-measure of sufficiently large mass m>0 with almost optimal carré du champ.
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来源期刊
Stochastic Processes and their Applications
Stochastic Processes and their Applications 数学-统计学与概率论
CiteScore
2.90
自引率
7.10%
发文量
180
审稿时长
23.6 weeks
期刊介绍: Stochastic Processes and their Applications publishes papers on the theory and applications of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests. Characterization, structural properties, inference and control of stochastic processes are covered. The journal is exacting and scholarly in its standards. Every effort is made to promote innovation, vitality, and communication between disciplines. All papers are refereed.
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