Analyzing Markov chains using Kronecker products : theory and applications

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...

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Bibliografische gegevens
Hoofdauteur: Dayar, Tužrul
Formaat: Livre numérique
Taal:Anglais
Gepubliceerd in: New York, NY : Springer New York [20..].
Cham : Springer Nature
Editie:1st ed. 2012.
Reeks:SpringerBriefs in Mathematics
Online toegang:Accès sur la plateforme de l'éditeur
Accès sur la plateforme Istex
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Opmerking: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Analyzing Markov chains using Kronecker products, theory and applications, Tužrul Dayar, 2012, New York, NY, Springer, 1 vol. (IX-86 p.), SpringerBriefs in mathematics, 978-1-4614-4189-2
• Analyzing Markov chains using Kronecker products, theory and applications, Tužrul Dayar, 2012, New York, NY, Springer, 1 vol. (IX-86 p.), SpringerBriefs in mathematics, 978-1-4614-4189-2
• Analyzing Markov Chains using Kronecker Products, Texte imprimé, 9781461441915
• Analyzing Markov chains using Kronecker products, theory and applications, Tužrul Dayar, 2012, New York, NY, Springer, 1 vol. (IX-86 p.), SpringerBriefs in mathematics, 978-1-4614-4189-2
• Analyzing Markov Chains using Kronecker Products, Texte imprimé, 9781461441915
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505 1 |a Introduction Background Kronecker representation Preprocessing Block iterative methods for Kronecker products Preconditioned projection methods Multilevel methods Decompositional methods Matrix analytic methods 
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520 |a 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 
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