随机 Volterra 积分方程的马尔可夫提升和渐近对数-哈纳克不等式

IF 1.1 2区 数学 Q3 STATISTICS & PROBABILITY
Yushi Hamaguchi
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引用次数: 0

摘要

我们引入了一个具有完全单调核的随机伏特拉积分方程(简称 SVIEs)的马尔可夫提升新框架。我们将马尔可夫提升的状态空间定义为可分离的希尔伯特空间,并将核的奇异性或规则性纳入定义中。我们证明了 SVIE 的解是由定义在希尔伯特空间上的提升随机演化方程(简称 SEE)的解来表示的,并证明了提升 SEE 解的存在性、唯一性和马尔可夫特性。此外,我们还通过渐近耦合方法建立了与马尔可夫提升相关的马尔可夫半群的渐近对数-哈纳克不等式和一些随之而来的性质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Markovian lifting and asymptotic log-Harnack inequality for stochastic Volterra integral equations

We introduce a new framework of Markovian lifts of stochastic Volterra integral equations (SVIEs for short) with completely monotone kernels. We define the state space of the Markovian lift as a separable Hilbert space which incorporates the singularity or regularity of the kernel into the definition. We show that the solution of an SVIE is represented by the solution of a lifted stochastic evolution equation (SEE for short) defined on the Hilbert space and prove the existence, uniqueness and Markov property of the solution of the lifted SEE. Furthermore, we establish an asymptotic log-Harnack inequality and some consequent properties for the Markov semigroup associated with the Markovian lift via the asymptotic coupling method.

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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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