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.

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Bibliographic Information

Book Title: Markov Set-Chains
Authors: Darald J. Hartfiel
Series Title: Lecture Notes in Mathematics
DOI: https://doi.org/10.1007/BFb0094586
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1998
Softcover ISBN: 978-3-540-64775-1Published: 20 August 1998
eBook ISBN: 978-3-540-68711-5Published: 14 November 2006
Series ISSN: 0075-8434
Series E-ISSN: 1617-9692
Edition Number: 1
Number of Pages: VIII, 132
Topics: Probability Theory and Stochastic Processes, Linear and Multilinear Algebras, Matrix Theory, Convex and Discrete Geometry, Mathematical and Computational Biology, Math Applications in Computer Science

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Table of contents (5 chapters)

Front Matter

Introduction

Stochastic matrices and their variants

Introduction to Markov set-chains

Convergence of Markov set-chains

Behavior in Markov set-chains

Back Matter

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