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Applied Probability Models with Optimization Applications
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Product Details
Author:
Sheldon M. Ross
Format:
Paperback
Pages:
224
Publisher:
Dover Publications (December 4, 1992)
Language:
English
ISBN-13:
9780486673141
ISBN-10:
0486673146
Weight:
9.04oz
Dimensions:
5.5" x 8.5"
Case Pack:
36
Series:
Dover Books on Mathematics
File:
Dover-Dover_05022026_P10034514_onix30_Complete-20260501.xml
Folder:
Dover
As low as:
$12.30
List Price:
$12.95
Publisher Identifier:
P-DOVER
Discount Code:
D
Audience:
College/higher education
Pub Discount:
65
Imprint:
Dover Publications
Overview
"A clarity of style and a conciseness of treatment which students will find most welcome. The material is valuable and well organized … an excellent introduction to applied probability." — Journal of the American Statistical Association.
This book offers a concise introduction to some of the stochastic processes that frequently arise in applied probability. Emphasis is on optimization models and methods, particularly in the area of decision processes. After reviewing some basic notions of probability theory and stochastic processes, the author presents a useful treatment of the Poisson process, including compound and nonhomogeneous Poisson processes. Subsequent chapters deal with such topics as renewal theory and Markov chains; semi-Markov, Markov renewal, and regenerative processes; inventory theory; and Brownian motion and continuous time optimization models.
Each chapter is followed by a section of useful problems that illustrate and complement the text. There is also a short list of relevant references at the end of every chapter. Students will find this a largely self-contained text that requires little previous knowledge of the subject. It is especially suited for a one-year course in applied probability at the advanced undergraduate or beginning postgraduate level. 1970 edition.
This book offers a concise introduction to some of the stochastic processes that frequently arise in applied probability. Emphasis is on optimization models and methods, particularly in the area of decision processes. After reviewing some basic notions of probability theory and stochastic processes, the author presents a useful treatment of the Poisson process, including compound and nonhomogeneous Poisson processes. Subsequent chapters deal with such topics as renewal theory and Markov chains; semi-Markov, Markov renewal, and regenerative processes; inventory theory; and Brownian motion and continuous time optimization models.
Each chapter is followed by a section of useful problems that illustrate and complement the text. There is also a short list of relevant references at the end of every chapter. Students will find this a largely self-contained text that requires little previous knowledge of the subject. It is especially suited for a one-year course in applied probability at the advanced undergraduate or beginning postgraduate level. 1970 edition.
