Analyzing Markov Chains using Kronecker Products

Tugrul Dayar

ISBN 10: 1461441897 / ISBN 13: 9781461441892

Published by Springer New York Jul 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Kronecker products are used to define the underlying Markov chain (MC) in various modeling formalisms, including compositional Markovian models, hierarchical Markovian models, and stochastic process algebras. The motivation behind using a Kronecker structured representation rather than a flat one is to alleviate the storage requirements associated with the MC. With this approach, systems that are an order of magnitude larger can be analyzed on the same platform. The developments in the solution of such MCs are reviewed from an algebraic point of view and possible areas for further research are indicated with an emphasis on preprocessing using reordering, grouping, and lumping and numerical analysis using block iterative, preconditioned projection, multilevel, decompositional, and matrix analytic methods. Case studies from closed queueing networks and stochastic chemical kinetics are provided to motivate decompositional and matrix analytic methods, respectively. 96 pp. Englisch.

Seller Inventory # 9781461441892

Bibliographic Details

• Title: Analyzing Markov Chains using Kronecker Products
• Publisher: Springer New York Jul 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1461441897
• ISBN 13: 9781461441892
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Kronecker products are used to define the underlying Markov chain (MC) in various modeling formalisms, including compositional Markovian models, hierarchical Markovian models, and stochastic process algebras. The motivation behind using a Kronecker structured representation rather than a flat one is to alleviate the storage requirements associated with the MC. With this approach, systems that are an order of magnitude larger can be analyzed on the same platform. The developments in the solution of such MCs are reviewed from an algebraic point of view and possible areas for further research are indicated with an emphasis on preprocessing using reordering, grouping, and lumping and numerical analysis using block iterative, preconditioned projection, multilevel, decompositional, and matrix analytic methods. Case studies from closed queueing networks and stochastic chemical kinetics are provided to motivate decompositional and matrix analytic methods, respectively.

From the Back Cover

None