Markov Set-Chains [electronic resource] / by Darald J. Hartfiel.

By: Hartfiel, Darald J [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextSeries: Lecture Notes in Mathematics ; 1695Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1998Description: VIII, 132 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540687115Subject(s): Mathematics | Computer science | Matrix theory | Biology -- Mathematics | Discrete groups | Distribution (Probability theory) | Mathematics | Probability Theory and Stochastic Processes | Linear and Multilinear Algebras, Matrix Theory | Convex and Discrete Geometry | Mathematical Biology in General | Math Applications in Computer ScienceAdditional physical formats: Printed edition:: No titleDDC classification: 519.2 LOC classification: QA273.A1-274.9QA274-274.9Online resources: Click here to access online
Contents:
Stochastic matrices and their variants -- to Markov set-chains -- Convergence of Markov set-chains -- Behavior in Markov set-chains.
In: Springer eBooksSummary: 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.
Item type: E-BOOKS
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Stochastic matrices and their variants -- to Markov set-chains -- Convergence of Markov set-chains -- Behavior in Markov set-chains.

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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