Applied Stochastic System Modeling
This book was written for an introductory one-semester or two-quarter course in shastic processes and their applications. The reader is assumed to have a basic knowledge of analysis and linear algebra at an undergraduate level. Shastic models are applied in many fields such as engineering systems, physics, biology, operations research, business, economics, psychology, and linguistics. Shastic modeling is one of the promising kinds of modeling in applied probability theory. This book is intended to introduce basic shastic processes: Poisson processes, renewal processes, discrete-time Markov chains, continuous-time Markov chains, and Markov-renewal processes. These basic processes are introduced from the viewpoint of elementary mathematics without going into rigorous treatments. This book also introduces applied shastic system modeling such as reliability and queueing modeling. Chapters 1 and 2 deal with probability theory, which is basic and prerequisite to the following chapters. Many important concepts of probabilities, random variables, and probability distributions are introduced. Chapter 3 develops the Poisson process, which is one of the basic and im portant shastic processes. Chapter 4 presents the renewal process. Renewal theoretic arguments are then used to analyze applied shastic models. Chapter 5 develops discrete-time Markov chains. Following Chapter 5, Chapter 6 deals with continuous-time Markov chains. Continuous-time Markov chains have im portant applications to queueing models as seen in Chapter 9. A one-semester course or two-quarter course consists of a brief review of Chapters 1 and 2, fol lowed in order by Chapters 3 through 6.
1000939695
Applied Stochastic System Modeling
This book was written for an introductory one-semester or two-quarter course in shastic processes and their applications. The reader is assumed to have a basic knowledge of analysis and linear algebra at an undergraduate level. Shastic models are applied in many fields such as engineering systems, physics, biology, operations research, business, economics, psychology, and linguistics. Shastic modeling is one of the promising kinds of modeling in applied probability theory. This book is intended to introduce basic shastic processes: Poisson processes, renewal processes, discrete-time Markov chains, continuous-time Markov chains, and Markov-renewal processes. These basic processes are introduced from the viewpoint of elementary mathematics without going into rigorous treatments. This book also introduces applied shastic system modeling such as reliability and queueing modeling. Chapters 1 and 2 deal with probability theory, which is basic and prerequisite to the following chapters. Many important concepts of probabilities, random variables, and probability distributions are introduced. Chapter 3 develops the Poisson process, which is one of the basic and im portant shastic processes. Chapter 4 presents the renewal process. Renewal theoretic arguments are then used to analyze applied shastic models. Chapter 5 develops discrete-time Markov chains. Following Chapter 5, Chapter 6 deals with continuous-time Markov chains. Continuous-time Markov chains have im portant applications to queueing models as seen in Chapter 9. A one-semester course or two-quarter course consists of a brief review of Chapters 1 and 2, fol lowed in order by Chapters 3 through 6.
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Applied Stochastic System Modeling
269Applied Stochastic System Modeling
269Paperback(Softcover reprint of the original 1st ed. 1992)
$54.99
54.99
In Stock
Product Details
ISBN-13: | 9783642846830 |
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Publisher: | Springer Berlin Heidelberg |
Publication date: | 12/16/2011 |
Edition description: | Softcover reprint of the original 1st ed. 1992 |
Pages: | 269 |
Product dimensions: | 6.69(w) x 9.61(h) x 0.02(d) |
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