Quan-Lin Li

Gebundenes Buch

Constructive Computation in Stochastic Models with Applications

The RG-Factorizations

Beschreibung

"Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic systems. It summarizes recent important advances in computational study of stochastic models from several crucial directions, such as stationary computation, transient solution, asymptotic analysis, reward processes, decision processes, sensitivity analysis as well as game theory. Graduate students, researchers and practicing engineers in the field of operations research, management sciences, applied probability, computer networks, manufacturing systems, transportation systems, insurance and finance, risk management and biological sciences will find this book valuable.

Dr. Quan-Lin Li is an Associate Professor at the Department of Industrial Engineering of Tsinghua University, China.

Produktdetails

• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin / Tsinghua University Press
• Artikelnr. des Verlages: 978-3-642-11491-5
• Seitenzahl: 650
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783642114915
• ISBN-10: 3642114911
• Artikelnr.: 28029193

Rezensionen

From the reviews:

"This 672-page book is the result of a colossal undertaking on the part of the author. ... Li's book includes extensive bibliographies at the end of each chapter. The book uses a wide variety of methods and creates a welcome unifying presentation. This book is for the serious researcher in stochastic models, and is a great book with which a young researcher might quickly move into serious analysis of applied queueing models." (Myron Hlynka, Mathematical Reviews, Issue 2011 f)

"This book deals with numerical ... methods for computing aspects of Markov chains, such as stationary and transient probability distributions, first passage times, and visiting times to certain states. ... this book is well organized, and should be a valuable reference for researchers and advanced graduate students working in numerical probability, structured matrices, etc. The results apply to large classes of stochastic models." (Charles Knessl, SIAM Review, Vol. 54 (1), 2012)