Introduction to Stochastic Processes (Paperback)

Cinlar Cinlar

ISBN 10: 0486497976 / ISBN 13: 9780486497976

Published by Dover Publications Inc., New York, 2013

About this Item

Paperback. This clear presentation of the most fundamental models of random phenomena employs methods that recognise computer-related aspects of theory. The text emphasizes the modern viewpoint, in which the primary concern is the behavior of sample paths. By employing matrix algebra and recursive methods, rather than transform methods, it provides techniques readily adaptable to computing with machines.Topics include probability spaces and random variables, expectations and independence, Bernoulli processes and sums of independent random variables, Poisson processes, Markov chains and processes, and renewal theory. Assuming some background in calculus but none in measure theory, the complete, detailed, and well-written treatment is suitable for engineering students in applied mathematics and operations research courses as well as those in a wide variety of other scientific fields. Many numerical examples, worked out in detail, appear throughout the text, in addition to numerous end-of-chapter exercises and answers to selected exercises. Clear presentation employs methods that recognize computer-related aspects of theory. Topics include expectations and independence, Bernoulli processes and sums of independent random variables, Markov chains, renewal theory, more. 1975 edition. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780486497976

Bibliographic Details

• Title: Introduction to Stochastic Processes (Paperback)
• Publisher: Dover Publications Inc., New York
• Publication Date: 2013
• Language: English
• ISBN 10: 0486497976
• ISBN 13: 9780486497976
• Binding: Paperback
• Condition: new

About this title

Synopsis

This clear presentation of the most fundamental models of random phenomena employs methods that recognize computer-related aspects of theory. The text emphasizes the modern viewpoint, in which the primary concern is the behavior of sample paths. By employing matrix algebra and recursive methods, rather than transform methods, it provides techniques readily adaptable to computing with machines.
Topics include probability spaces and random variables, expectations and independence, Bernoulli processes and sums of independent random variables, Poisson processes, Markov chains and processes, and renewal theory. Assuming some background in calculus but none in measure theory, the complete, detailed, and well-written treatment is suitable for engineering students in applied mathematics and operations research courses as well as those in a wide variety of other scientific fields. Many numerical examples, worked out in detail, appear throughout the text, in addition to numerous end-of-chapter exercises and answers to selected exercises.

About the Author

Erhan Çinlar is a Professor of Operations Research and Financial Engineering at Princeton University. He was formerly on the faculty of Northwestern University.