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。     

非时齐马氏过程的耦合基本定理

本文将耦合方法应用于非时齐马氏过程,推广了时齐情形的耦合基本定理,为后续研究非时齐马氏过程的耦合提供了理论基础.     

抽象空间中的 q-过程的遍历位势

<正> §1.引言Syski 在[1]中,对时齐的可数状态的遍历的马氏过程,在其二阶矩存在及其它条件下,证明了遍历位势核的存在性,并利用位势核的种种性质,改善了著名的 Riesz 分解定理.本文讨论的是时齐的一般状态的马氏过程,在强遍历的条件下,证明了遍历位势核的存在性,并得到了遍历位势核的一些性质.利用遍历位势核的存在性,改善了一般状态的马氏过程的 Riesz 分解定理.此外,还讨论了如何利用转移密度函数,寻找转移函数的遍历极限的问题.     

可数马尔科夫过程的强马氏性

§1 引 理 对非齐次马尔科夫过程转移概率的分析性质及非齐次可数马科夫过程样本函数的性质,人们已做了较为系统的研究。本文讨论的是非齐次可数马尔科夫过程(以下简称为马氏链)的强马氏性问题,这是马氏链基本理论的一个重要组成部分。若一个右标准马氏链可分、Borel可测且右下半连续,则称其为右正则马氏链(详见定义2.1)。本文首先指出:任何一个右标准马氏链都有右正则修正;继而,通过考察推移过程的性质,证明了:任何右正则马氏链均具有强马氏性。从而在右标准马氏链情形,本文将〔6〕第二章§4§6中过程右     

概率最优停止问题

本文在时齐马氏序列中引入了概率最优停时和(ε,B)概率最优停时的概念,得到了其显式表达式,从而在某种程度上弥补了期望最优时的不足.同时,本文研究了两种停止问题的关系,指出期望最优停时也是概率最优停时的特例,并证明了集合首达时也是一种概率最优时,进一步给出了首达时为有限的等价条件.     

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.     

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。     

马尔可夫链在研究血型分布规律上的应用

本文用马尔可夫链为工具证明了代与代之间血型遗传过程是一个时齐的马尔可夫链.若用 p、q、r 分别表示 O、A、B 三个血型基因出现的概率,则(OO,AA,BB,OA,OB,AB)六种血型的稳定分布为(p~2,q~2,r~2,2pq,2pr,2qr).     

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     

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.     

一类Markov链的强逼近

本文给出了一类非时齐的Markov链的强逼近.作为应用,建立了临床试验中Markov链自适应设计的强相合性,重对数律和弱收敛.     

Strong Approximations of Martingale Vectors and Their Applications in Markov-Chain Adaptive Designs

The strong approximations of a class of R~d-valued martingales are considered.The conditions usedin this paper are easier to check than those used in [3] and [9].As an application,the strong approximation ofa class of non-homogenous Markov chains is established,and the asymptotic properties are established for themulti-treatment Markov chain adaptive designs in clinical trials.     

非齐次树上马氏链的若干强偏差定理

通过构造适当的非负鞅,将Doob鞅收敛定理应用于几乎处处收敛的研究,给出了一类非齐次树上马氏链场加权和滑动平均的若干强偏差定理.     

On the class of Markov chains with finite convergence time

We study the necessary and sufficient conditions for a finite ergodic Markov chain to converge in a finite number of transitions to its stationary distribution. Using this result, we describe the class of Markov chains which attain the stationary distribution in a finite number of steps, independent of the initial distribution. We then exhibit a queueing model that has a Markov chain embedded at the points of regeneration that falls within this class. Finally, we examine the class of continuous time Markov processes whose embedded Markov chain possesses the property of rapid convergence, and find that, in the case where the distribution of sojourn times is independent of the state, we can compute the distribution of the system at time t in the form of a simple closed expression.     

A chain of evolution algebras

We introduce a notion of chain of evolution algebras. The sequence of matrices of the structural constants for this chain of evolution algebras satisfies an analogue of Chapman-Kolmogorov equation. We give several examples (time homogenous, time non-homogenous, periodic, etc.) of such chains. For a periodic chain of evolution algebras we construct a continuum set of non-isomorphic evolution algebras and show that the corresponding discrete time chain of evolution algebras is dense in the set. We obtain a criteria for an evolution algebra to be baric and give a concept of a property transition. For several chains of evolution algebras we describe the behavior of the baric property depending on the time. For a chain of evolution algebras given by the matrix of a two-state evolution we define a baric property controller function and under some conditions on this controller we prove that the chain is not baric almost surely (with respect to Lebesgue measure). We also construct examples of the almost surely baric chains of evolution algebras. We show that there are chains of evolution algebras such that if it has a unique (resp. infinitely many) absolute nilpotent element at a fixed time, then it has unique (resp. infinitely many) absolute nilpotent element any time; also there are chains of evolution algebras which have not such property. For an example of two dimensional chain of evolution algebras we give the full set of idempotent elements and show that for some values of parameters the number of idempotent elements does not depend on time, but for other values of parameters there is a critical time t_c such that the chain has only two idempotent elements if time t?t_c and it has four idempotent elements if time t<t_c.     

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本文的目的是要研究Cayley树图上奇偶马氏链场的渐近均分割性\bd 首先我们给出Cayley树 图上奇偶马氏链场关于状态和状态序偶出现频率的强大数定律, 然后证明其具有a.e.收敛性 的渐近均分割性\bd     

树上二重非齐次马氏链随机转移概率的极限性质

研究树上二重非齐次马氏链随机转移概率的调和平均极限性质,作为推论,得到了树上非齐次马氏链以及非齐次马氏链上的随机转移概率调和平均极限性质.     

On the use of periodicity properties for the efficient numerical solution of certain Markov chains

Markov chains with periodic graphs arise frequently in a wide range of modelling experiments. Application areas range from flexible manufacturing systems in which pallets are treated in a cyclic manner to computer communication networks. The primary goal of this paper is to show how advantage may be taken of this property to reduce the amount of computer memory and computation time needed to compute stationary probability vectors of periodic Markov chains. After reviewing some basic properties of Markov chains whose associated graph is periodic, we introduce a ‘reduced scheme’ in which only a subset of the probability vector need be computed. We consider the effect of the application of both direct and iterative methods to the original Markov chain (permuted to normal periodic form) as well as to the reduced system. We show how the periodicity of the Markov chain may be efficiently computed as the chain itself is being generated. Finally, some numerical experiments to illustrate the theory, are presented.     

Stopping times for quantum Markov chains

In the paper we introduce stopping times for quantum Markov states. We study algebras and maps corresponding to stopping times, give a condition of strong Markov property and give classification of projections for the property of accessibility. Our main result is a new recurrence criterium in terms of stopping times (Theorem 1 and Corollary 2). As an application of the criterium we study how, in Section 6, the quantum Markov chain associated with the one-dimensional Heisenberg (usually non-Markovian) process, obtained from this quantum Markov chain by restriction to a diagonal subalgebra, is such that all its states are recurrent. We were not able to obtain this result from the known recurrence criteria of classical probability.Supported by GNAFA-CNR, Bando n. 211.01.25.     

Intensity models and transition probabilities for credit card loan delinquencies

We estimate the probability of delinquency and default for a sample of credit card loans using intensity models, via semi-parametric multiplicative hazard models with time-varying covariates. It is the first time these models, previously applied for the estimation of rating transitions, are used on retail loans. Four states are defined in this non-homogenous Markov chain: up-to-date, one month in arrears, two months in arrears, and default; where transitions between states are affected by individual characteristics of the debtor at application and their repayment behaviour since. These intensity estimations allow for insights into the factors that affect movements towards (and recovery from) delinquency, and into default (or not). Results indicate that different types of debtors behave differently while in different states. The probabilities estimated for each type of transition are then used to make out-of-sample predictions over a specified period of time.