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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.
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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