Markov调制风险模型的轨道刻划和概率构造

用随机过程的轨道,严格地刻划了Markov调制风险模型U=(Q,G,F;J,s,X),它是已有的Markov调制风险模型的一般化.基于模型U,分别给出带保费率向量C和带税率向量γ的Markov调制风险过程R~u={R~u(t),t≥0}和R~u(γ)={R~u(γ,t),t≥0}.给定特征组A=(Q,G,F),用概率方法构造了模型U.从而为用随机过程理论和方法研究Markov调制风险模型和过程,奠定了严实的随机过程基础.     

客户回报随机过程的Markov性和时间齐次性

在客户发展关系的Markov链模型的基础上,构建了企业的客户回报随机过程．证明了：在适当假设下,客户回报过程是Markov链。甚至是时间齐次的Markov链．本文求出了该链的转移概率．通过转移概率得到了客户给企业期望回报的一些计算公式,从而为企业选定发展客户关系策略提供了有效的量化基础．     

通过消息监控识别罪犯的Markov模型

通过消息监控识别罪犯是一个十分有意义的实际问题。本文采用Markov模型的方法,将整个消息传递网络看作一个犯罪传递的Markov链.根据所收集到的消息估计出两个节点（人）之间犯罪传递的概率,得到一个Markov概率转移矩阵,并求出网络长期运行下去的稳定解,作为各节点（人）参与犯罪程度的度量。文章通过实例说明该方法的有效性。     

一类二维Markov跳跃非线性时滞系统的镇定控制

研究一类二维Markov跳跃非线性时滞系统的镇定控制问题.给出了Markov跳跃非线性时滞系统解的存在唯一性的一个充分条件,以及系统依概率全局渐近稳定的判别准则.通过构造适当形式的Lyapunov函数,采用积分反推方法给出了一类二维Markov跳跃非线性时滞系统的无记忆状态反馈控制器.证明了在该控制律的作用下,闭环系统平衡点依概率全局渐近稳定.     

GI^（1）＋GI^（2）／G／1排队模型

对于GI^(1) GI^(2)/G/I排队模型,本借助献[1]中引入的Markov骨架过程方法求出了此模型到达过程,等待时间及队长的概率分布。     

批量马尔可夫到达过程概述

批量马尔可夫到达过程(Batch Markovian Arrival Processes,BMAP)对平稳点过程类具有稠密性,能够描述许多到达过程,在计算机、可靠性、通信和库存等领域的随机建模中获得广泛应用,是一类非常重要的随机点过程.通过对BMAP理论主要文献的分析,系统介绍了BMAP的概念和主要性质,回顾了BMAP应用成果和拟合工作的发展,展望了BMAP理论的发展前景.     

半马氏过程的一维分布及构造

本文求出了半马氏过程跳跃链的转移概率,给出了半马氏过程的逗留时间分布和一维分布,构造了半马氏过程$X(t,\omega)$,最后证明了半马氏过程的两种定义是等价的.     

Markov链在生态学中的一个应用

Markov链是随机过程的一个特例,专门研究在无后效条件下时间和状态均为离散的随机转移问题.本文运用与Markov链相关的转移概率矩阵性质,探讨一个鱼类洄游实际问题的数学模型,寻求鱼类洄游的数量规律.     

随机环境中的Markov 过程的构造及等价定理

引进了随机转移矩阵, p-m过程和随机环境中的Markov过程等基本概念, 并且给出了一些例子, 特别是给出了由非时齐的密度函数构造随机转移函数的例子. 我们从p-m过程构造了随机环境中的Markov过程和绕积Markov过程, 并且研究了随机环境中的Markov过程、本原过程、环境过程和绕积Markov过程的一些性质. 给出了随机环境中的Markov过程的几个等价定理.     

Markov积分算子半群的限制及关于增加积分算子半群的生成

证明了转移函数是l∞的一个子空C^1上的正的压缩C0半群，其极小生成元恰好是Markov积分算子半群的生成元在C^1中的部分；Markov积分算子半群的生成元稠定的充分必要条件是q-矩阵Q一致有界；同时转移函数是Feller—Reuter—Riley的充要条件是Markov积分算子半群的生成元在Cn中的部分产生一个强连续半群．最后，在序Banach空间给出了增加的压缩积分算子半群的生成定理．     

Adjoining batch Markov arrival processes of a Markov chain

A batch Markov arrival process (BMAP) X* = (N, J) is a 2-dimensional Markov process with two components, one is the counting process N and the other one is the phase process J. It is proved that the phase process is a time-homogeneous Markov chain with a finite state-space, or for short, Markov chain. In this paper, a new and inverse problem is proposed firstly: given a Markov chain J, can we deploy a process N such that the 2-dimensional process X* = (N, J) is a BMAP? The process X* = (N, J) is said to be an adjoining BMAP for the Markov chain J. For a given Markov chain the adjoining processes exist and they are not unique. Two kinds of adjoining BMAPs have been constructed. One is the BMAPs with fixed constant batches, the other one is the BMAPs with independent and identically distributed (i.i.d) random batches. The method we used in this paper is not the usual matrix-analytic method of studying BMAP, it is a path-analytic method. We constructed directly sample paths of adjoining BMAPs. The expressions of characteristic (D _k , k = 0, 1, 2 · · ·) and transition probabilities of the adjoining BMAP are obtained by the density matrix Q of the given Markov chain J. Moreover, we obtained two frontal Theorems. We present these expressions in the first time.     

一类四元数受限矩阵表达式的最大秩（英文）

确立了一类分块矩阵M11 M12 XM21 M22 M23Y M32 M33的最大秩公式,其中,X和Y是两个受限于四元数线性矩阵方程A_1X=C_1,XB_1=C_2,A_3XB_3=C_3,A_2Y=D_1,YB_2=D_2.的变量矩阵。作为该公式的一项应用,我们推导出上述矩阵方程解集等同于另一四元数二次矩阵方程组解集的条件。     

Double-markov risk model

Given a new Double-Markov risk model DM=(μ,Q,ν,H;Y,Z) and Double-Markov risk process U={U(t),t≥ 0}. The ruin or survival problem is addressed. Equations which the survival probability satisfied and the formulas of calculating survival probability are obtained. Recursion formulas of calculating the survival probability and analytic expression of recursion items are obtained. The conclusions are expressed by Q matrix for a Markov chain and transition probabilities for another Markov Chain.     

On a BMAP/G/1 G-queue with setup times and multiple vacations

In this paper, we consider a BMAP/G/1 G-queue with setup times and multiple vacations. Arrivals of positive customers and negative customers follow a batch Markovian arrival process (BMAP) and Markovian arrival process (MAP) respectively. The arrival of a negative customer removes all the customers in the system when the server is working. The server leaves for a vacation as soon as the system empties and is allowed to take repeated (multiple) vacations. By using the supplementary variables method and the censoring technique, we obtain the queue length distributions. We also obtain the mean of the busy period based on the renewal theory.     

Optimal consumption and investment problem with random horizon in a BMAP model

In this paper, we consider the consumption and investment problem with random horizon in a Batch Markov Arrival Process (BMAP) model. The investor invests her wealth in a financial market consisting of a risk-free asset and a risky asset. The price processes of the riskless asset and the risky asset are modulated by a continuous-time Markov chain, which is the phase process of a BMAP. The possible consumption or investment are restricted to a sequence of random discrete time points which are determined by the same BMAP. The investor has only consumption opportunities at some of these random time points, has both consumption and investment opportunities at some other random time points, and can do nothing at the remaining random time points. The object of the investor is to select the consumption–investment strategy that maximizes the expected total discounted utility. The purpose of this paper is to analyze the impact of the consumption–investment opportunity and the economic state on the value functions and consumption–investment strategies. The general solution and the exact solution under the assumption that the consumption and the terminal wealth are evaluated by the power utility are obtained. Finally, a numerical example is presented.     

On the BMAP/G/1 G-queues with second optional service and multiple vacations

This paper deals with an BMAP/G/1 G-queues with second optional service and multiple vacations. Arrivals of positive customers and negative customers follow a batch Markovian arrival process (BMAP) and Markovian arrival process (MAP), respectively. After completion of the essential service of a customer, it may go for a second phase of service. The arrival of a negative customer removes the customer being in service. The server leaves for a vacation as soon as the system empties and is allowed to take repeated (multiple) vacations. By using the supplementary variables method and the censoring technique, we obtain the queue length distributions. We obtain the mean of the busy period based on the renewal theory.     

一类数字限制集的上、下Assouad维数

设b≥2,D_1,D_2■{0,1,...,b-1},S_1,S_2■N且S_1,S_2不交.记E是由下面(1.1)所确定的数字限制集.本文讨论了E的各种分形维数,主要证明了E的上、下Assouad维数公式.     

Stochastic finite-time boundedness of Markovian jumping neural network with uncertain transition probabilities

The stochastic finite-time boundedness problem is considered for a class of uncertain Markovian jumping neural networks (MJNNs) that possess partially known transition jumping parameters. The transition of the jumping parameters is governed by a finite-state Markov process. By selecting the appropriate stochastic Lyapunov–Krasovskii functional, sufficient conditions of stochastic finite time boundedness of MJNNs are presented and proved. The boundedness criteria are formulated in the form of linear matrix inequalities and the designed algorithms are described as optimization ones. Simulation results illustrate the effectiveness of the developed approaches.     

Exponential bounds for discrete-time singularly perturbed Markov chains

This paper develops exponential type upper bounds for scaled occupation measures of singularly perturbed Markov chains in discrete time. By considering two-time scale in the Markov chains, asymptotic analysis is carried out. The cases of the fast changing transition probability matrix is irreducible and that are divisible into l ergodic classes are examined first; the upper bounds of a sequence of scaled occupation measures are derived. Then extensions to Markov chains involving transient states and/or nonhomogeneous transition probabilities are dealt with. The results enable us to further our understanding of the underlying Markov chains and related dynamic systems, which is essential for solving many control and optimization problems.