Elements of Applied Stochastic Processes (Probability & Mathematical Statistics)

Bhat, U. Narayan:

ISBN 10: 0471071994 / ISBN 13: 9780471071990

Edité par John Wiley & Sons Inc, 1972

A propos de cet article

432 Seiten ex Library Book / aus einer wissenschafltichen Bibliothek / Sprache: Englisch Gewicht in Gramm: 969 23,9 x 15,7 x 2,5 cm, Gebundene Ausgabe.

N° de réf. du vendeur 366748

Détails bibliographiques

• Titre: Elements of Applied Stochastic Processes (Probability & Mathematical Statistics)
• Éditeur: John Wiley & Sons Inc
• Date de parution: 1972
• Langue: anglais
• ISBN à 10 chiffres: 0471071994
• ISBN à 13 chiffres: 9780471071990
• Reliure: Couverture rigide
• État de l'article: Gut
• Catalogues du vendeur:
  • Alle Bücher

A propos de ce titre

Synopsis

Book by Bhat U Narayan

Quatrième de couverture

Praise for THE SECOND EDITION

"A valuable contribution . . . rigorous and carefully thought out."
Zeitschrift fur Operations Research

A state–of–the–art text on stochastic models and their applications

Much has changed in the field of stochastic modeling since the highly successful Second Edition of this popular text. In response, the authors have significantly revised their book to deliver a thoroughly up–to–date overview of the field.

This Third Edition of Elements of Applied Stochastic Processes provides a basic understanding of the fundamental theory of stochastic processes. Topics include Markov chains, and Markov, branching, renewal, and stationary processes, all of which are illustrated with the rich diversity of actual applications. Restructured to enhance the book s usefulness for practicing professionals, students, and instructors, this edition features two chapters dedicated entirely to applications from journal articles and new material on statistical inference for stochastic processes, with inference on queues as an area of application. Also new is a chapter on simulation and Markov Chain Monte Carlo.

This updated new edition:

• Retains the bridge between theory and application while improving teachability
• Integrates a broad set of applications into the text
• Provides expanded coverage on statistical inference for stochastic processes
• Utilizes a wealth of examples from research papers and monographs
• Offers a comprehensive introduction to stationary processes and time series analysis