Regenerative Stochastic Simulation PDF Download
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Author: Gerald S. Shedler
Publisher: Elsevier
ISBN: 0080925723
Category : Mathematics
Languages : en
Pages : 412
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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: 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 : 60
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Book Description
Author: M. A. Crane
Publisher: Springer
ISBN:
Category : Case method
Languages : en
Pages : 126
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Book Description
The purpose of this report is to provide an introduction to the regenerative method for simulation analysis. The simulations are simulations of stochastic systems, i.e., systems with random elements. The regenerative approach leads to a statistical methodology for analyzing the output of those simulations which have the property of 'starting afresh probabilistically' from time to time. The class of such simulations is very large and very important, including simulations of a broad variety of queues and queueing networks, inventory systems, inspection, maintenance, and repair operations, and numerous other situations.
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: G.S. Shedler
Publisher:
ISBN:
Category :
Languages : it
Pages : 0
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Book Description
Author: G. S. Shedler
Publisher:
ISBN:
Category :
Languages : en
Pages : 48
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Book Description
Author: International Business Machines Corporation. Research Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 30
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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: D. L. Iglehart
Publisher: Springer
ISBN:
Category : Language Arts & Disciplines
Languages : en
Pages : 230
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Book Description
Author: Francis Linus Gunther
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Book Description
The regenerative method for stochastic system simulation allows data collection each time the stochastic process enters a specific single state, r, called the regeneration state. The generated observations have the desireable property of being independent and identically distributed. Relative to a fixed run length, however, the mean time between entries into r may be excessively long for complicated stochastic systems, thus providing few observations and poor variance estimates. The almost regenerative method is an extension of the regenerative method designed to alleviate this problem for complicated stochastic systems (such as a network of queues). The almost regenerative method allows data collection each time the stochastic process enters a set of states. Simulations of simple queueing networks show that the almost regenerative method can provide an order to magnitude improvement over the regenerative method in terms of the mean-square-error of the estimator of total delay in queue, and this relative improvement increases with system complexity.
Author: Gerald S. Shedler
Publisher: Springer Science & Business Media
ISBN: 146121050X
Category : Mathematics
Languages : en
Pages : 232
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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.
