
Elements of Applied Stochastic Processes
Wiley (Publisher)
3rd Edition
Published on 24. September 2002
Book
Hardback
XVI, 472 pages
978-0-471-41442-1 (ISBN)
Description
Integration of theory and application offers improved teachability.
* Provides a comprehensive introduction to stationary processes and time series analysis.
* Integrates a broad set of applications into the text.
* Utilizes a wealth of examples from research papers and monographs.
Reviews / Votes
"...provides excellent coverage of the basic topics...Bhat and Miller have provided an excellent text and reference book." (Interfaces, July/ August 2004) "...an extended and well-written introduction to the theory...of stochastic processes and their applications..." (Zentralblatt Math, Vol. 1024, 2004)"...besides conveying the concepts of stochastic processes, this book succeeds in providing insight into the reasons why for a particular topic certain lines of investigation are pursued and why certain variables/functions are introduced." (Technometrics, Vol. 45, No. 3, August 2003)
More details
Product info
gebunden
Series
Edition
3. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
College/higher education
Professional and scholarly
Edition type
New edition
Product notice
sewn/stitched
Cloth over boards
Illustrations
Charts: 10 B&W, 0 Color; Tables: 28 B&W, 0 Color; Graphs: 5 B&W, 0 Color
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 30 mm
Weight
888 gr
ISBN-13
978-0-471-41442-1 (9780471414421)
Schweitzer Classification
Other editions
Previous edition
U. Narayan Bhat
Elements of Applied Stochastic Analysis
Book
10/1984
2nd Edition
Wiley
€183.22
Article exhausted; check for reprint
Persons
U. NARAYAN BHAT, PhD, is Professor of Statistical Science and Operations Research as well as the Dean of Research and Graduate Studies at Southern Methodist University.
GREGORY K. MILLER, PhD, is Associate Professor of Statistics at Stephen F. Austin State University.
Content
Preface.
Stochastic Processes: Description and Definition.
Markov chains.
Irreducible Markov Chains with Ergodic States.
Branching Processes and Other Special Topics.
Statistical Inference for Markov Chains.
Applied Markov Chains.
Simple Markov Processes.
Statistical Inference for Simple Markov Processes.
Applied Markov Processes.
Renewal Processes.
Stationary Processes and Time Series Analysis.
Simulation and Markov Chain Monte Carlo.
Answers to Selected Exercises.
Appendix.
Author Index.
Subject Index.