Regenerative Simulation of Non-Markovian Stochastic Systems PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Regenerative Simulation of Non-Markovian Stochastic Systems PDF full book. Access full book title Regenerative Simulation of Non-Markovian Stochastic Systems by International Business Machines Corporation. Research Division. Download full books in PDF and EPUB format.
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
Get Book Here
Book Description
Discrete-event simulations are often non-Markovian in the sense that the underlying stochastic process of the simulation cannot be modeled as a Markov chain with countable state space. We discuss regenerative simulation methods for non-Markovian systems whose underlying stochastic process can be represented as a generalized semi-Markov process. Applications to modeling and simulation of ring and bus networks are given. Keywords include: Regenerative simulation; Generalized semi-Markov processes; Non-Markovian systems; Recurrence and regeneration; Ring and bus networks.
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
Get Book Here
Book Description
Discrete-event simulations are often non-Markovian in the sense that the underlying stochastic process of the simulation cannot be modeled as a Markov chain with countable state space. We discuss regenerative simulation methods for non-Markovian systems whose underlying stochastic process can be represented as a generalized semi-Markov process. Applications to modeling and simulation of ring and bus networks are given. Keywords include: Regenerative simulation; Generalized semi-Markov processes; Non-Markovian systems; Recurrence and regeneration; Ring and bus networks.
Author: Gerald S. Shedler
Publisher: Elsevier
ISBN: 0080925723
Category : Mathematics
Languages : en
Pages : 412
Get Book Here
Book Description
Simulation is a controlled statistical sampling technique that can be used to study complex stochastic systems when analytic and/or numerical techniques do not suffice. The focus of this book is on simulations of discrete-event stochastic systems; namely, simulations in which stochastic state transitions occur only at an increasing sequence of random times. The discussion emphasizes simulations on a finite or countably infinite state space.* Develops probabilistic methods for simulation of discrete-event stochastic systems* Emphasizes stochastic modeling and estimation procedures based on limit theorems for regenerative stochastic processes* Includes engineering applications of discrete-even simulation to computer, communication, manufacturing, and transportation systems* Focuses on simulations with an underlying stochastic process that can specified as a generalized semi-Markov process* Unique approach to simulation, with heavy emphasis on stochastic modeling* Includes engineering applications for computer, communication, manufacturing, and transportation systems
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 27
Get Book Here
Book Description
A generalized semi-Markov process provides a stochastic process model for a discrete-event simulation. This representation is particularly useful for non-Markovian systems where it is nontrivial to obtain recurrence properties of the underlying stochastic processes. The authors develop geometric trials arguments which can be used to obtain results on recurrence and regeneration in this setting. Such properties are needed to establish estimation procedures based on regenerative processes. Applications to modeling and simulation of ring and bus networks are discussed. (Author).
Author:
Publisher:
ISBN:
Category : Military research
Languages : en
Pages : 272
Get Book Here
Book Description
Author: Gerald S. Shedler
Publisher: Springer Science & Business Media
ISBN: 146121050X
Category : Mathematics
Languages : en
Pages : 232
Get Book Here
Book Description
Networks of queues arise frequently as models for a wide variety of congestion phenomena. Discrete event simulation is often the only available means for studying the behavior of complex networks and many such simulations are non Markovian in the sense that the underlying stochastic process cannot be repre sented as a continuous time Markov chain with countable state space. Based on representation of the underlying stochastic process of the simulation as a gen eralized semi-Markov process, this book develops probabilistic and statistical methods for discrete event simulation of networks of queues. The emphasis is on the use of underlying regenerative stochastic process structure for the design of simulation experiments and the analysis of simulation output. The most obvious methodological advantage of simulation is that in principle it is applicable to stochastic systems of arbitrary complexity. In practice, however, it is often a decidedly nontrivial matter to obtain from a simulation information that is both useful and accurate, and to obtain it in an efficient manner. These difficulties arise primarily from the inherent variability in a stochastic system, and it is necessary to seek theoretically sound and computationally efficient methods for carrying out the simulation. Apart from implementation consider ations, important concerns for simulation relate to efficient methods for generating sample paths of the underlying stochastic process. the design of simulation ex periments, and the analysis of simulation output.
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category : Markov processes
Languages : en
Pages : 60
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 564
Get Book Here
Book Description
Author: Peter W. Glynn
Publisher:
ISBN:
Category :
Languages : en
Pages : 79
Get Book Here
Book Description
A wide variety of stochastic systems may be viewed as Markov chains taking on values in a general state space. An example is the class of generalized semi-Markov processes, which are commonly obtained in network queueing problems via the technique of supplementary variables. A simulator is often interested in obtaining steady state properties of such a system. Some recent developments in Markov chain theory by Athreya, Ney, and Nummelin allow one to embed a certain subclass of these processes in a regenerative environment. We study some consequences of this embedding and develop statistical estimation procedures for the general problem that bear close resemblance to the regenerative method of simulation analysis for finite state Markov chains. (Author).
Author: Michael A. Crane
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
Get Book Here
Book Description
An earlier developed technique for analyzing simulations of GI/G/S queues and Markov chains is shown to apply to discrete-event simulations which can be modeled as regenerative processes. It is possible to address questions of simulation run duration and of starting and stopping simulations because of the existence of a random grouping of observations which produces independent identically distributed blocks in the course of the simulation. This grouping allows one to obtain confidence intervals for a general function of the steady-state distribution of the process being simulated and for the asymptotic cost per unit time. The technique is illustrated with a simulation of a retail inventory distribution system. (Author).
Author: Oliver Ibe
Publisher: Newnes
ISBN: 0124078397
Category : Mathematics
Languages : en
Pages : 515
Get Book Here
Book Description
Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. - Presents both the theory and applications of the different aspects of Markov processes - Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented - Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.
