Markov Chains: Models, Algorithms and Applications

  • Wai-Ki Ching
  • Michael K. Ng
Book

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 83)

Table of contents

About this book

Introduction

Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models.

Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems.

Keywords

Algorithms Business and management applications Markov chain Markov chains Markov decision process Markov model Optimization models Stochastic networks

Authors and affiliations

  • Wai-Ki Ching
    • 1
  • Michael K. Ng
    • 2
  1. 1.The University of Hong KongHong KongP.R. China
  2. 2.Hong Kong Baptist UniversityHong KongP.R. China

Bibliographic information

  • DOI https://doi.org/10.1007/0-387-29337-X
  • Copyright Information Springer Science+Business Media, Inc. 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-29335-6
  • Online ISBN 978-0-387-29337-0
  • Series Print ISSN 0884-8289
  • About this book