Discovers 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 variety of fields. Editor(s): Latouche, Guy; Ramaswami, V.; Sethuraman, Jay; Sigman, Karl; Squillante, Mark; Yao, David. Series: Springer Proceedings in Mathematics and Statistics. Num Pages: 258 pages, biography. BIC Classification: KJT; PBKS; PBT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 15. Weight in Grams: 573. . 2012. Hardback. . . . . Books ship from the US and Ireland.

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Bibliographic Details

Title: Matrix-Analytic Methods in Stochastic Models
Publisher: Springer-Verlag New York Inc.
Publication Date: 2012
Language: English
ISBN 10: 1461449081
ISBN 13: 9781461449089
Binding: Hardcover
Condition: New

About this title

Synopsis

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/or applications perspective, as well as identify directions for future research.

About the Author

Guy Latouche, Universit� Libre de Bruxelles, Belgium
Vaidyanathan Ramaswami , AT&T Labs Research, USA

Jay Sethuraman, Columbia University, USA

Karl Sigman, Columbia University, USA
Mark S. Squillante, IBM Thomas J. Watson Research Center, USA

David D. Yao, Columbia University, USA

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