全稳定Q过程构造的一个等价条件

给出了Q过程构造的等价条件,证明了Q过程的构造等价于一个条件方程组的解,以此方法求出了所有的Q过程、F过程、B过程及BF过程的等价条件。     

一类两指标过程—EARMA过程

本文将两指标 ARMA 过程加以推广,提出了一类新的两指标弱平稳过程——EARMA 过程,给出了它的平稳性条件和可逆性条件;讨论了它的若干性质;并讨论了它的参数估计,其中主要是自相关估计的强相合速度。     

布朗运动的若干结果

1827年,英国生物学家布朗(R.Brown)观察到微小的粒子在液体(或气体)中不停地作极不规则的运动,这一发现在科学界产生了很大的影响。时间已经过去165年,关于这种运动的数学研究论文仍然层出不穷。国际上著名的几种概率论杂志中,几乎每期都有关于布朗运动的论文。1 一维布朗运动从发现到理论的建立经历了漫长的时间,其中有许多卓越人物的工作。1905年,爱因斯坦认为运动是由于微粒受到液体中大量分子的碰撞(每秒10~(20)次)而引起的。微粒在t时的位置x_t是随机的。假设x_0=0,以p(t,x)表x_t的一个坐标分量的分布密度,在一定条件下,他得到。     

一维扩散过程的最优停止问题的研究

本文研究了一维扩散过程的最优停止问题,论证了W iener过程和几何布朗运动是F e ller过程,同时给出了一般扩散过程的处理方法.     

一类分支过程的强遍历性

通过与生灭过程进行比较,得到了一类分支过程强遍历性的充分条件,对已有的结果进行了推广.     

有关对偶方程的充要条件

本文给出了多维马氏过程的对偶过程存在的充分必要条件,在多维跳过程的情形下,本文给出了用Q-对来验证对偶过程是否存在的条件。     

多类顾客多服务台队列网络的高负荷极限定理

多类顾客多服务台队列网络广泛地应用到计算机网络、通讯网络和交通网络 .由于系统的复杂性 ,其数量指标的精确解很难求出 .为了寻求逼近解 ,本文用概率测度弱收敛理论对进行了研究 ,在高负荷的条件下 ,我们获得了网输入过程、闲时过程和负荷过程的极限定理 .     

混合过程的刻画

研究了接触过程和非紧邻的排它过程的混合过程,得到混合过程除在临界点λc(β)处外,它的平稳分布总是光滑的,并且混合过程有一定的稳定性.     

Markov骨架过程的构造

本文讨论了Markov骨架过程的构造问题,把Dovb构造Markov过程的方法推广到Markov骨架过程的构造.     

服务中断的带优先反馈排队系统弱极限定理

利用了概率论中的弱极限定理,研究了服务中断的带优先反馈排队系统弱极限定理,得到了队长,虚等待时间及逗留时间的弱极限定理.     

随机脉冲干扰对B—CKdV方程扭状孤波解的影响

本文利用侯振挺等^[1]提出的马尔可夫骨架过程理论求出了在随机随冲干扰下,B－CKdV方程ut auux bu^2ux ruxx uxxx=0的行波解及其一维分布,并研究了行波解的渐近性质。     

马尔可夫骨架过程在种群动态学中的应用

本文借助于马尔可夫骨架过程（舱妒）方法研究了种群动态学中单种群种群数量的瞬时分布，并证明单种群种群数量在时刻t的瞬时分布是某一非负线性方程的最小非负解。     

The minimal solution of a Markov renewal equation

Given a killed Markov process, one can use a procedure of Ikedaet al. to revive the process at the killing times. The revived process is again a Markov process and its transition function is the minimal solution of a Markov renewal equation. In this paper we will calculate such solutions for a class of revived processes.     

更新理论推理过程及其应用

更新过程和马尔可夫更新过程中均有相对应的更新方程,实际问题中有许多变量都满足更新方程.但是在运用更新方程时,对于一些感兴趣的变量很难直接套用更新方程,这就使更新方程在实际问题的应用有许多困难.针对这个问题,总结归纳了应用更新方程的更新理论推理过程,给出了具体的推理方法和步骤,并举例进行了说明.     

Markov process functionals in finance and insurance

The Markov property of Markov process functionals which are frequently used in economy, finance, engineering and statistic analysis is studied. The conditions to judge Markov property of some important Markov process functionals are presented, the following conclusions are obtained： the multidimensional process with independent increments is a multidimensional Markov process; the functional in the form of path integral of process with independent increments is a Markov process; the surplus process with the doubly stochastic Poisson process is a vector Markov process. The conditions for linear transformation of vector Markov process being still a Markov process are given.     

The construction of markov processes in random environments and the equivalence theorems

In sec.1, we introduce several basic concepts such as random transition function, p-m process and Markov process in random environment and give some examples to construct a random transition function from a non-homogeneous density function. In sec. 2, we construct the Markov process in random enviromment and skew product Markov process by p -m process and investigate the properties of Markov process in random environment and the original process and environment process and skew product process. In sec. 3, we give several equivalence theorems on Markov process in random environment.     

马氏骨架过程理论在再生分支过程中的应用

本文旨在将经典的分支过程进行推广到再生分支过程,进而采用马氏骨架过程理论,特别是Doob骨架过程理论研究再生分支过程,得到它的瞬时分布和极限分布。     

Lumpability and Commutativity of Markov Processes

Abstract

We introduce the concepts of lumpability and commutativity of a continuous time discrete state space Markov process, and provide a necessary and sufficient condition for a lumpable Markov process to be commutative. Under suitable conditions we recover some of the basic quantities of the original Markov process from the jump chain of the lumped Markov process.     

Second order limit theorems for the Markov branching process in random environments

In a Markov branching process with random environments, limiting fluctuations of the population size arise from the changing environment, which causes random variation of the ‘deterministic’ population prediction, and from the stochastic wobble around this ‘deterministic’ mean, which is apparent in the ordinary Markov branching process. If the random environment is generated by a suitable stationary process, the first variation typically swamps the second kind. In this paper, environmental processes are considered which, in contrast, lead to sampling and environmental fluctuation of comparable magnitude. The method makes little use either of stationarity or of the branching property, and is amenable to some generalization away from the Markov branching process.     

Estimating Stochastic Dynamical Systems Driven by a Continuous-Time Jump Markov Process

We discuss the use of a continuous-time jump Markov process as the driving process in stochastic differential systems. Results are given on the estimation of the infinitesimal generator of the jump Markov process, when considering sample paths on random time intervals. These results are then applied within the framework of stochastic dynamical systems modeling and estimation. Numerical examples are given to illustrate both consistency and asymptotic normality of the estimator of the infinitesimal generator of the driving process. We apply these results to fatigue crack growth modeling as an example of a complex dynamical system, with applications to reliability analysis.