© 2010

Constructive Computation in Stochastic Models with Applications

The RG-Factorization


Table of contents

  1. Front Matter
    Pages i-xiv
  2. Quan-Lin Li
    Pages 1-71
  3. Quan-Lin Li
    Pages 72-130
  4. Quan-Lin Li
    Pages 131-175
  5. Quan-Lin Li
    Pages 176-215
  6. Quan-Lin Li
    Pages 216-287
  7. Quan-Lin Li
    Pages 288-330
  8. Quan-Lin Li
    Pages 331-388
  9. Quan-Lin Li
    Pages 389-431
  10. Quan-Lin Li
    Pages 432-525
  11. Quan-Lin Li
    Pages 526-573
  12. Back Matter
    Pages 652-672

About this book


"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.


Analysis Markov Markov Chain Markov Chains Markov decision process Markov renewal process Sage Stochastic model Stochastic models algorithm game theory operations research optimization

Authors and affiliations

  1. 1.Department of Industrial EngineeringTsinghua UniversityBeijingP. R. China

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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)