用概率距离研究非时齐马氏链的收敛性

该文用概率距离和耦合方法研究了一般状态空间非时齐马氏链的收敛性,得到了一般状态空间.非时齐马氏链收敛的一个条件.     

非时齐马氏过程的对数Sobolev不等式

本文将时齐马氏过程的经典对数Sobolev不等式推广到非时齐马氏过程,建立了非时齐马氏过程的转移半群与对数Sobolev不等式之间的关系.     

非时齐马氏过程的Liggett-Stroock不等式

本文将时齐马氏过程中重要的代数不等式Liggett-Stroock不等式推广到非时齐马氏过程中,建立了非时齐马氏过程的转移半群与Liggett-Stroock不等式之间的关系.     

非时齐准转移函数及其所对应的两个单参数半群

众所周知，由于双参数半群的局限，使得对时齐马氏过程的许多结果无法在非时齐马氏过程得到，或得到的结果也较时齐的情况粗糙得多．本文给出了与非时齐准转移函数对应的两个单参数半群，研究了二者的性质，并证明了二者相互唯一决定。这就提供了一条由单参数压缩半群研究非时齐马氏过程的途径。     

连续时间非时齐马氏过程的广义Dobrushin系数的估计

本文研究了非时齐马氏过程的广义Dobrushin系数的估计问题.在将经典Dobrushin遍历系数推广为加权的遍历系数的基础上,利用了矩阵拆分的方法,得到了对这种广义遍历系数的估计方法,推广了时齐马氏过程关于遍历系数的估计结果,借此可进一步得到有关遍历性的判定结论.     

抽象空间中的马氏过程的强遍历性及收敛速度

<正> §1.引言Doob 在[2]中对一般状态的时齐的马氏过程的遍历性理论,作了系统的研究,得到了完满的结果.D.G.Kendall 在[8]中,J.F.C.Kingman 在[6]、[7]中,D.Vere-Jones在[5]中,对可数状态的时齐的马氏过程的遍历极限的收敛速度,作了研究,这些文章的一个共同特点是:假定对某一状态其遍历极限的收敛速度为几何速度(指数速度),证明对其它状态,其遍历极限的收敛速度亦然.然而 D.Isaacson 在[1]中,研究了可数状态时齐的马氏过程的强遍历性,而且证明了强遍历性蕴含了收敛速度是几何速度(指数速度).本文研究的是一般状态的马氏过程(时齐的或非时齐的),得到了马氏过程满足强遍历性的各种充要条件;证明了强遍历性蕴含了收敛的指数速度;找出了最佳收敛速度;并证明了在什么条件下达到最佳收敛速度.     

一个非时齐马氏过程

<正> 本文研究了一个非时齐马氏过程,考虑其瞬时性质与渐近性质.过程的实际背景是成批到来无限束的 M(t)/M(t)/∞排队问题,排队过程的到来的强度,批量的大小与服务的强度均随时间变化.§1.引言由于实践范围的扩大,若干重要马氏过程的非时齐情况在近年来都得到深入研究.本文旨在考虑一个出现于排队论中的非时齐马氏过程 (在排队论中非时齐过程的重要性早已在[2]中指出过).     

无界报酬非时齐折扣马氏决策模型

在文献[1]—[3]中在各自的条件下,讨论过非时齐折扣马氏决策模型及其ε(≥0)最优策略存在的条件.在文献[4],文献[5]中,在状态和行动集都是可数的条件下,讨论了具有绝对平均相对有界的无界报酬的时齐折扣马氏决策模型.本文在状态集仍为可数,行动集为任意的条件下,建立与[4]相应的非时齐的折扣马氏决策模型;给出模型的有限阶段逼近和建立最优方程;证明了ε(>0)最优马氏策略的存在性和行动集为有限集时最优     

随机环境中具有随机控制函数的受控分枝过程

对随机环境中具有随机控制函数的受控分枝过程进行了更为详尽的概率描述和直观解释;证明了此过程是时齐马氏链和随机环境中的马氏链,并对其概率母函数及矩量进行了讨论。     

抽象空间中非时齐马氏过程的分析理论(Ⅰ)——连续性、可微性及柯氏方程式

<正> 马氏过程的分析理论(这是主要指马氏过程的转移函数及其对应的半群的分析理论),是马氏过程基本理论的一个重要组成部分,对时齐的马氏过程的分析理论,不论其状态空间是可数的或一般的,国内外作者都进行过大量研究,得到过一系列结果.然而,对     

非时齐马氏过程的随机单调性

本文研究了非时齐马氏过程的随机单调性问题.利用时齐的马氏过程随机单调性的相关证明方法,加以改进,获得了非时齐马氏过程随机单调性的显式判定方法,并进一步将这一充分性条件推广为等价条件.     

Successful couplings for a class of stochastic differential equations driven by Lvy processes

By constructing proper coupling operators for the integro-differential type Markov generator,we establish the existence of a successful coupling for a class of stochastic differential equations driven by L’evy processes.Our result implies a new Liouville theorem for space-time bounded harmonic functions with respect to the underlying Markov semigroups,and it is sharp for Ornstein-Uhlenbeck processes driven by α-stable L’evy processes.     

Recent Results on Recurrent Solutions and Limit Distributions of SDEs

The limit distribution for homogeneous Markov processes is studied extensively and well understood, but it is not the case for inhomogeneous Markov processes. In this paper, we review some recent results on inhomogeneous Markov processes generated by non-autonomous stochastic (partial) differential equations (SDE in short). Under some suitable conditions, we show that the distribution of recurrent solutions of SDEs constitutes the limit distribution of the corresponding inhomogeneous Markov processes.     

Exponential Convergence Rates of Markov Chains under a Weaken Minorization Condition

In this paper, we obtain the quantitative bound of the exponential convergence rates of Markov chains under a weaken minorization condition, using the coupling method and the analytic approach. And also, we obtain the convergence rates for continuous time Markov processes.     

Strong Law of Large Numbers and Central Limit Theorems for Functionals of Inhomogeneous Semi-Markov Processes

Limit theorems for functionals of classical (homogeneous) Markov renewal and semi-Markov processes have been known for a long time, since the pioneering work of Pyke Schaufele (Limit theorems for Markov renewal processes, Ann. Math. Statist., 35(4):1746–1764, 1964). Since then, these processes, as well as their time-inhomogeneous generalizations, have found many applications, for example, in finance and insurance. Unfortunately, no limit theorems have been obtained for functionals of inhomogeneous Markov renewal and semi-Markov processes as of today, to the best of the authors’ knowledge. In this article, we provide strong law of large numbers and central limit theorem results for such processes. In particular, we make an important connection of our results with the theory of ergodicity of inhomogeneous Markov chains. Finally, we provide an application to risk processes used in insurance by considering a inhomogeneous semi-Markov version of the well-known continuous-time Markov chain model, widely used in the literature.     

Existence of the optimal measurable coupling and ergodicity for Markov processes

The existence theorem of the optimal measurable coupling of two probability kernels on a complete separable metric measurable space is proved. Then by this theorem, a general ergodicity theorem for Markov processes is obtained. And as an immediate application to particle systems the uniqueness theorem of the stationary distribution is supplemented, i.e. the uniqueness theorem also implies the existence of the stationary distribution.     

MEXIT: Maximal un-coupling times for stochastic processes

Classical coupling constructions arrange for copies of the same Markov process started at two different initial states to become equal as soon as possible. In this paper, we consider an alternative coupling framework in which one seeks to arrange for two different Markov (or other stochastic) processes to remain equal for as long as possible, when started in the same state. We refer to this “un-coupling” or “maximal agreement” construction as MEXIT, standing for “maximal exit”. After highlighting the importance of un-coupling arguments in a few key statistical and probabilistic settings, we develop an explicit MEXIT construction for stochastic processes in discrete time with countable state-space. This construction is generalized to random processes on general state-space running in continuous time, and then exemplified by discussion of MEXIT for Brownian motions with two different constant drifts.     

Successful couplings for a class of stochastic differential equations driven by Lévy processes

By constructing proper coupling operators for the integro-differential type Markov generator, we establish the existence of a successful coupling for a class of stochastic differential equations driven by Lévy processes. Our result implies a new Liouville theorem for space-time bounded harmonic functions with respect to the underlying Markov semigroups, and it is sharp for Ornstein-Uhlenbeck processes driven by ??-stable Lévy processes.     

Déviations modérées pour la moyennisation d'une EDS avec petite diffusion

We prove in this Note the moderate deviation principle (MDP) for the averaging principle of a stochastic differential equation (SDE) in a fast random environment, modelized by an exponentially ergodic Markov process independent of the Wiener process driving the SDE. The main tools will be the method of Puhalskii for exponential tightness and a MDP for inhomogeneous functionals of Markov processes established in [5].