The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes.

The literature on Bayesian methods for the analysis of discrete-time semi-Markov processes is sparse. In this paper, we introduce the semi-Markov beta-Stacy process, a stochastic process useful for the Bayesian non-parametric analysis of semi-Markov processes. The semi-Markov beta-Stacy process is conjugate with respect to data generated by a semi-Markov process, a property which makes it easy to obtain probabilistic forecasts. Its predictive distributions are characterized by a reinforced random walk on a system of urns.

     

Stochastic integral with respect to a semi-Markov process of diffusion type

We consider a multidimensional semi-Markov process of diffusion type. A stochastic integral with respect to the semi-Markov process is defined in terms of asymptotics related to the first exit time from a small neighborhood of the starting point of the process, and, in particular, in terms of its characteristic operator. This integral is equal to the sum of two other integrals: the first one is a curvilinear integral with respect to an additive functional defined in terms of the expected first exit time from a small neighborhood, and the second one is a stochastic integral with respect to a martingale of special kind. To prove the existence and to derive the properties of the integral, both the method of deducing sequences and that of inscribed ellipsoids are used. For Markov processes of diffusion type, the new definition of the stochastic integral is reduced to the standard one. Bibliography: 8 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 328, 2005, pp. 251–276.     

Nonparametric Estimation for Semi-Markov Processes Based on its Hazard Rate Functions

The problem of estimating the Markov renewal matrix and the semi-Markov transition matrix based on a history of a finite semi-Markov process censored at time T (fixed) is addressed for the first time. Their asymptotic properties are studied. We begin by the definition of the transition rate of this process and propose a maximum likelihood estimator for the hazard rate functions and then we show that this estimator is uniformly strongly consistent and converges weakly to a normal random variable. We construct a new estimator for an absolute continous semi-Markov kernel and give detailed derivation of uniform strong consistency and weak convergence of this estimator as the censored time tends to infinity. This revised version was published online in June 2006 with corrections to the Cover Date.     

Analysis of a single server queue with semi-Markovian service interruption

In this paper we analyze a discrete-time single server queue where the service time equals one slot. The numbers of arrivals in each slot are assumed to be independent and identically distributed random variables. The service process is interrupted by a semi-Markov process, namely in certain states the server is available for service while the server is not available in other states. We analyze both the transient and steady-state models. We study the generating function of the joint probability of queue length, the state and the residual sojourn time of the semi-Markov process. We derive a system of Hilbert boundary value problems for the generating functions. The system of Hilbert boundary value problems is converted to a system of Fredholm integral equations. We show that the system of Fredholm integral equations has a unique solution. This revised version was published online in June 2006 with corrections to the Cover Date.     

Stochastic integral in the case of infinite expected first exit time

We develop a method of analysis of a multidimensional semi-Markov process of diffusion type in the case of infinite expectation of the first exit time from a small neighborhood of the initial point. A generalization of Dynkin’s formula for this case is obtained. Itô’s formula for a stochastic integral over a multidimensional semi-Markov process of diffusion type is derived. Bibliography: 4 titles.     

Semi-Markov failure rates processes

Usually, a reliability function is defined by a failure rate which is a real function taking the non-negative real values. In this paper the failure rate is assumed to be a stochastic process with non-negative and right continuous trajectories. The reliability function is defined as an expectation of a function of that random process. Particularly, the failure rate defined by the semi-Markov processes is considered here. The theorems dealing with the renewal equations for the conditional reliability functions with a semi-Markov process as a failure rate are presented in this paper. A system of that kind of equations for the discrete state space semi-Markov process is applied for calculating the reliability function for the 3-states semi-Markov random walk. Using the introduced system of renewal equations for the countable state space, the reliability function for the Furry-Yule failure rate process is obtained.     

Semi-Markov processes and α-invariant distributions

A semi-Markov process is easily made Markov by adding some auxiliary random variables. This paper discusses the I-type quasi-stationary distributions of such “extended” processes, and the α-invariant distributions for the corresponding Markov transition probabilities; and we show that there is an intimate relation between the two. The results have relevance in the study of the time to “absorption” or “death” of semi-Markov processes. The particular case of a terminating renewal process is studied as an example.     

A Non-Homogeneous Continuous Time Semi-Markov Model for the Study of Accumulated Claim Process

The accumulated claim process is the summed total of all claims starting from time t. The semi-Markov environment, at authors’ opinion, is able to follow the evolution of this process. In the paper a continuous time non-homogeneous semi-Markov model with a denumerable set of states will be used to follow the stochastic evolution of the accumulated claim process.     

Equations for second moments of solutions of a system of linear differential equations with random semi-Markov coefficients and random input

We derive equations that determine second moments of a random solution of a system of Itô linear differential equations with coefficients depending on a finite-valued random semi-Markov process. We obtain necessary and sufficient conditions for the asymptotic stability of solutions in the mean square with the use of moment equations and Lyapunov stochastic functions.

     

一类马氏环境中连续型传染病模型的灭绝概率

这篇文章主要研究一类马氏环境中的连续型传染病模型,即假设疾病传染率和病人减少(死亡或治愈)的发生频率及数目都受一外在马氏过程的影响.在这些假设下,我们得出初始状态为i时疾病的灭绝概率满足的积分方程,并通过Laplace-变换的方法,给出了积分方程的解.进一步,当外在马氏环境为两个状态,并且每次病人减少的数目都服从指数分布时,给出了灭绝概率Laplace-变换的明确表达式.     

Queuing Systems with Semi-Markov Flow in Average and Diffusion Approximation Schemes

We study asymptotic average and diffusion approximation schemes for semi-Markov queuing systems by a random evolution approach and using compensating operator of the corresponding extended Markov renewal process. These results generalize Markov and renewal flow queuing systems.      

Diffusion processes with delay at the endpoints of a segment

A continuous semi-Markov process with a segment as the range of values is considered. This process coincides with a diffusion process inside the segment, i.e., up to the first hitting time of the boundary of the segment and at any time when the process leaves the boundary. The class of such processes consists of Markov processes with reflection at the boundaries (instantaneously or with a delay) and semi-Markov processes with intervals of constancy on some boundary. We derive conditions of existence of such a process in terms of a semi-Markov transition generating function on the boundary. The method of imbedded alternating renewal processes is applied to find a stationary distribution of the process. Bibliography: 3 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 351, 2007, pp. 284–297.     

Operational queueing system in the scheme of diffusion approximation with evolution averaging

We consider an operational queuing system of the type [SM|M|∞]^N in the scheme of diffusion approximation. The queueing system is described by a semi-Markov random evolution. __________ Translated from Ukrains’kyi Matematychnyi Zhurnal, Vol. 58, No. 5, pp. 708–714, May, 2006.     

Large Closed Queueing Networks in Semi-Markov Environment and Their Application

The paper studies closed queueing networks containing a server station and k client stations. The server station is an infinite server queueing system, and client stations are single-server queueing systems with autonomous service, i.e. every client station serves customers (units) only at random instants generated by a strictly stationary and ergodic sequence of random variables. The total number of units in the network is N. The expected times between departures in client stations are (N μ _j )⁻¹. After a service completion in the server station, a unit is transmitted to the jth client station with probability p _j (j=1,2,…,k), and being processed in the jth client station, the unit returns to the server station. The network is assumed to be in a semi-Markov environment. A semi-Markov environment is defined by a finite or countable infinite Markov chain and by sequences of independent and identically distributed random variables. Then the routing probabilities p _j (j=1,2,…,k) and transmission rates (which are expressed via parameters of the network) depend on a Markov state of the environment. The paper studies the queue-length processes in client stations of this network and is aimed to the analysis of performance measures associated with this network. The questions risen in this paper have immediate relation to quality control of complex telecommunication networks, and the obtained results are expected to lead to the solutions to many practical problems of this area of research.      

Additive functionals of regular semi-Markov processes

The behavior of the mean values of additive functionals of regular semi-Markov processes with arbitrary (not necessarily finite or countable) sets of states is studied. An integral representation of the mean value of an additive functional is obtained. The behavior of certain operators connected with the process is investigated. As as illustration of the possible applications of the results obtained here we formulate and prove a limit theorem for a semi-Markov process. See [7].     

Application of the Superposition of Renewal Processes to the Study of Batch Arrival Queues

The G /G/1-type batch arrival system is considered. We deal with non-steady-state characteristics of the system like the first busy period and the first idle time, the number of customers served on the first busy period. The study is based on a generalization of Korolyuk's method which he developed for semi-Markov random walks.     

Insensitivity of Generalized Semi-Markov Processes Evolving in a Random Environment

The theory of insensitivity within generalized semi-Markov processes is extended to cover the case where such a process evolves in a random environment; that is, when the decay rates and transition probabilities are functions of the state of an extraneous environmental process.     

Random processes of manifolds in R n

Summary We compute the expected values of certain random variables associated with a random process of manifolds in R ⁿ by inserting certain general formulas of integral geometry into the definition of the moment measures of a point process.Dedicated to Professor Leopold Schmetterer on the occasion of his 60^th Birthday     

Dual approximation of random evolutions in an averaging scheme

We consider a double approximation of semi-Markov random evolutions, namely, the averaging and diffusion approximation, when the balance condition is not fulfilled. Double approximation algorithms are applicable for reserve and transport processes and other stochastic systems in a semi-Markov random medium.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 44, No. 3, pp. 400–408, March, 1992.     

Continuous procedure of stochastic approximation in a semi-Markov medium

Using the Lyapunov function for an averaged system, we establish conditions for the convergence of the procedure of stochastic approximation