In this study extending classical Markov chain theory to handle fluctuating transition matrices, the author develops a theory of Markov set-chains and provides numerous examples showing how that theory can be applied. Chapters are concluded with a discussion of related research. Readers who can benefit from this monograph are those interested in, or involved with, systems whose data is imprecise or that fluctuate with time. A background equivalent to a course in linear algebra and one in probability theory should be sufficient.
Author(s): Darald J. Hartfiel (auth.)
Series: Lecture Notes in Mathematics 1695
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 1998
Language: English
Pages: 132
City: New York
Tags: Probability Theory and Stochastic Processes; Linear and Multilinear Algebras, Matrix Theory; Convex and Discrete Geometry; Mathematical Biology in General; Math Applications in Computer Science
Introduction....Pages 1-2
Stochastic matrices and their variants....Pages 3-25
Introduction to Markov set-chains....Pages 27-57
Convergence of Markov set-chains....Pages 59-89
Behavior in Markov set-chains....Pages 91-113
