GI/G/1排队系统队长的强大数定律和中心极限定理

本文首先证明当服务强度小于1时,GI/G/1排队系统的队长是一个特殊的马尔可夫骨架过程——正常返的Doob骨架过程,然后运用马尔可夫骨架过程的强大数定律和中心极限定理等重要结果,给出了队长的累积过程的期望和方差,并给出了该累积过程满足强大数定律和中心极限定理的充分条件。     

马尔可夫骨架过程的极限分布

本文运用基本更新定理和Smith关键更新定理等理论和方法,对马尔可夫骨架过程的极限分布进行深入研究,得到主要结果如下:去掉了原有结果中要求的绝对连续的条件,给出了马尔可夫骨架过程极限分布存在的充分条件;得到了马尔可夫骨架过程极限分布的具体公式,并证明了该极限分布为概率分布.     

隐非齐次马尔可夫模型的强大数定律

在状态集都有限的情况下,给出了隐马尔可夫模型的一些性质定理．利用马氏链的强极限定理,得到了隐非齐次马尔可夫模型的强大数定律．     

隐非齐次马尔可夫模型的强大数定律

本文研究了一类隐非齐次马尔可夫模型的强极限定理.利用鞅差序列收敛定理,获得了观测链{Y_n,n≥0}的强大数定律,并给出了观测链的Shannon-McMillan定理.     

随机变量的截尾与任意随机变量序列的强极限定理

利用随机变量的截尾研究任意随机变量序列的性质,建立了一类矩条件下任意随机变量序列的强极限定理.作为推论,得到了可列非齐次马尔可夫过程的一个强极限定理,推广了鞅差序列当1≤p≤2和p≥2时的Chow定理,相应的一些已有结果和若干经典的关于独立随机变量序列的强大数定律是本文的特例。     

马氏链集间转移的一类强大数定律

本文研究了马氏链从一个状态子集到另一个状态子集的转移概率的极限性质.利用Doob鞅收敛定理,获得了任意随机序列的强大数定律、马氏链泛函的强大数定律和强遍历定理.推广了马氏链传统转移概率的极限性质和强极限定理.     

半马尔可夫过程的极限分布及其推广

本文运用马尔可夫骨架过程的极限理论研究齐次可列半马尔可夫过程,得到其极限分布.当更新间隔的分布不是格子分布时,本文的结果和邓永录等[1]中的结果一致,但采用的方法不同,本文采用的是马尔可夫骨架过程的理论方法,而[1]中采用的是交替更新过程的方法;而且关于更新间隔服从格子分布的情形,[1]中没有研究,而本文给出了结果.最...     

关于~*-mixing随机变量序列的强大数定律

利用任意随机变量序列的强大数定律讨论 * -mixing随机变量序列的强大数定律 ,得到了该序列的一个强极限定理 ,推广了经典的 * mixing随机变量序列的强大数定律 .同时讨论了 m相依序列和独立随机变量序列的强大数定律 .     

随机变量的截尾与几个经典强大数定律的推广

利用随机变量的截尾方法和条件三级数定理这一工具研究任意随机变量序列的性质,得到了矩条件下任意随机变量序列的一类强极限定理和强大数定律以及一些简单实用的结论,推广了与此相应的一些结果和若干经典的强大数定律.     

关于树指标马氏链强极限定理的若干研究

树指标随机过程是近年来概率论的研究方向之一,已引起了概率论、物理学、计算机等学科的广泛关注.树指标马氏链是一类重要的树指标随机过程.近年来,作者与其合作者利用研究概率论强极限定理的新方法,在树指标马氏链的强极限定理方面得出一系列研究成果,其中包括树指标马氏链的若干强大数定律和Shannon-McMillan定理.本文系统地介绍了这些工作,特别地,详细介绍了作者在树指标马氏链的强大数定律和Shannon-McMillan方面的工作,并结合一个定理的证明,介绍所用的方法.     

Uniform limit theorems for Harris recurrent Markov chains

Summary We study uniform limit theorems for regenerative processes and get strong law of large numbers and central limit theorem of this type. Then we apply those results to Harris recurrent Markov chains based on some ideas of K. Athreya, P. Ney and E. Nummelin.     

Branching Markov processes and related asymptotics

Models for Markov processes indexed by a branching process are presented. The new class of models is referred to as the branching Markov process (BMP). The law of large numbers and a central limit theorem for the BMP are established. Bifurcating autoregressive processes (BAR) are special cases of the general BMP model discussed in the paper. Applications to parameter estimation are also presented.     

STRONG LAW OF LARGE NUMBERS AND SHANNON-MCMILLAN THEOREM FOR MARKOV CHAINS FIELD ON CAYLEY TREE

1 lntroductionA tr(t(t is a grapl1 G = {T, E) wllicl1 is co1l11ected a11d colltai1ls no cir(.uits. Give11 a11y twov(irtic'is 't / P E T, let crP bc tl1e ullique patl1 col111ecti11g,v aIld /]. Defille tl1e graph distal1cc(l(rr, p) to hc the Ilu1llber of edges co11tained in the path crP.We discuss ulainly tl1e rooted Cayley tree TC,2(i.e.,binary tree. See Fig.1). In tlle Cayleytree Tc,2,tl1e root (denoted by 0) llas OIlly two 11(tiglll)(irs al1d all otller vertices have threeneighbors. A…     

关于树指标非齐次马氏链的广义熵遍历定理

主要研究了树指标非齐次马氏链的广义熵遍历定理.首先证明了树指标非齐次马氏链上的二元函数延迟平均的强极限定理.然后得到了树指标非齐次马氏链上状态出现延迟频率的强大数定律,以及树指标非齐次马氏链的广义熵遍历定理.作为推论,推广了一些已有结果.同时,证明了局部有限无穷树树指标有限状态随机过程广义熵密度的一致可积性.     

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.     

Law of large numbers and central limit theorem for randomly forced PDE's

We consider a class of dissipative PDE's perturbed by an external random force. Under the condition that the distribution of perturbation is sufficiently non-degenerate, a strong law of large numbers (SLLN) and a central limit theorem (CLT) for solutions are established and the corresponding rates of convergence are estimated. It is also shown that the estimates obtained are close to being optimal. The proofs are based on the property of exponential mixing for the problem in question and some abstract SLLN and CLT for mixing-type Markov processes.     

Periodically correlated autoregressive Hilbertian processes

We consider periodically correlated autoregressive processes in Hilbert spaces. Our studies on these processes involve existence, covariance structure, estimation of the covariance operators, strong law of large numbers and central limit theorem.     

Large deviations of Markov chains with multiple time-scales

For Markov processes evolving on multiple time-scales a combination of large component scalings and averaging of rapid fluctuations can lead to useful limits for model approximation. A general approach to proving a law of large numbers to a deterministic limit and a central limit theorem around it have already been proven in Kang and Kurtz (2013) and Kang et al. (2014). We present here a general approach to proving a large deviation principle in path space for such multi-scale Markov processes. Motivated by models arising in systems biology, we apply these large deviation results to general chemical reaction systems which exhibit multiple time-scales, and provide explicit calculations for several relevant examples.     

关于可列马氏链状态出现频率延迟平均的强大数定律

对于齐次马氏链由有限状态推广到可列的情形,由于可列和与极限运算不能交换,与文中证明方法与有限情形不同.利用了二元函数延迟平均的强极限定理和条件期望的平滑性,研究可列齐次马氏链状态出现频率延迟平均的强大数定律.