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Item Details
Title: SEMI-MARKOV PROCESSES AND RELIABILITY
By: Nikolaos Limnios, G. Oprisan
Format: Paperback

List price: £89.99


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 1461266408
ISBN 13: 9781461266402
Publisher: SPRINGER-VERLAG NEW YORK INC.
Pub. date: 21 October, 2012
Edition: Softcover reprint of the original 1st ed. 2001
Series: Statistics for Industry and Technology
Pages: 222
Synopsis: At first there was the Markov property. The theory of stochastic processes, which can be considered as an exten- sion of probability theory, allows the modeling of the evolution of systems through the time. It cannot be properly understood just as pure mathemat- ics, separated from the body of experience and examples that have brought it to life. The theory of stochastic processes entered a period of intensive develop- ment, which is not finished yet, when the idea of the Markov property was brought in. Not even a serious study of the renewal processes is possible without using the strong tool of Markov processes. The modern theory of Markov processes has its origins in the studies by A. A: Markov (1856-1922) of sequences of experiments "connected in a chain" and in the attempts to describe mathematically the physical phenomenon known as Brownian mo- tion. Later, many generalizations (in fact all kinds of weakenings of the Markov property) of Markov type stochastic processes were proposed. Some of them have led to new classes of stochastic processes and useful applications.Let us mention some of them: systems with complete connections [90, 91, 45, 86]; K-dependent Markov processes [44]; semi-Markov processes, and so forth. The semi-Markov processes generalize the renewal processes as well as the Markov jump processes and have numerous applications, especially in relia- bility.
Illustrations: biography
Publication: US
Imprint: Springer-Verlag New York Inc.
Returns: Returnable
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DISCRETE-TIME SEMI-MARKOV RANDOM EVOLUTIONS AND THEIR APPLICATIONS (HB)
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INTRODUCTION TO STOCHASTIC MODELS
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MATHEMATICAL METHODS FOR SURVIVAL ANALYSIS, RELIABILITY (HB)
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