ENGLISH

Matrix-Analytic Methods in Stochastic Models

By Guy Latouche , David D. Yao (eds.) , Karl Sigman , Jung Woo Baek , Ho Woo Lee , Se Won Lee (auth.) , Vaidyanathan Ramaswami , Jay Sethuraman , Mark S. Squillante

Book information

Publisher
Springer-Verlag New York
Year
2013
ISBN
978-1-4614-4908-9, 978-1-4614-4909-6
DOI
10.1007/978-1-4614-4909-6
Language
english
Format
PDF
Filesize
4 MB (3850665 bytes)
Series
Springer Proceedings in Mathematics & Statistics 27
Edition
1
Pages
258\264
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Management ScienceProbability Theory and Stochastic Processes; Numer

Description

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.

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