Matrix-Analytic Methods in Stochastic Models

  • Guy Latouche
  • Vaidyanathan Ramaswami
  • Jay Sethuraman
  • Karl Sigman
  • Mark S. Squillante
  • David D. Yao
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 27)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Jung Woo Baek, Ho Woo Lee, Se Won Lee, Soohan Ahn
    Pages 1-24
  3. Mogens Bladt, Luz Judith R. Esparza, Bo Friis Nielsen
    Pages 41-56
  4. Giuliano Casale, Peter G. Harrison
    Pages 57-85
  5. Sophie Hautphenne, Guy Latouche, Giang T. Nguyen
    Pages 87-106
  6. Qi-Ming He, Hanqin Zhang, Juan C. Vera
    Pages 107-121
  7. Guy Latouche, Giang T. Nguyen, Zbigniew Palmowski
    Pages 187-207
  8. Back Matter
    Pages 251-256

About these proceedings

Introduction

Matrix-analytic and related methods have become recognized as an important and fundamental approach for the mathematical analysis of general classes of complex stochastic models.  Research in the area of matrix-analytic and related methods seeks to discover underlying probabilistic structures intrinsic in such stochastic models, develop numerical algorithms for computing functionals (e.g., performance measures) of the underlying stochastic processes, and apply these probabilistic structures and/or computational algorithms within a wide variety of fields.  This volume presents recent research results on: the theory, algorithms and methodologies concerning matrix-analytic and related methods in stochastic models; and the application of matrix-analytic and related methods in various fields, which includes but is not limited to computer science and engineering, communication networks and telephony, electrical and industrial engineering, operations research, management science, financial and risk analysis, and bio-statistics.  These research studies provide deep insights and understanding of the stochastic models of interest from a mathematics and applications perspective, as well as identify directions for future research.

Keywords

Brownian Motion Matrix-Analytic Methods Operations research Queueing Networks Stochastic Models

Editors and affiliations

  • Guy Latouche
    • 1
  • Vaidyanathan Ramaswami
    • 2
  • Jay Sethuraman
    • 3
  • Karl Sigman
    • 4
  • Mark S. Squillante
    • 5
  • David D. Yao
    • 6
  1. 1., Faculté des SciencesUniv. Libre de BruxellesBruxellesBelgium
  2. 2.At&T Labs ResearchFlorham ParkUSA
  3. 3., IEORColumbia UniversityNew York CityUSA
  4. 4., Dept of Industrial Engineering and ORColumbia UniversityNew YorkUSA
  5. 5.IBM Thomas J. Watson Research CenterYorktown HeightsUSA
  6. 6., IEORColumbia UniversityNew York CityUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4909-6
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4908-9
  • Online ISBN 978-1-4614-4909-6
  • Series Print ISSN 2194-1009
  • Series Online ISSN 2194-1017