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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Analyzing Markov Chains Using Kronecker Products: Theory and Applications

Analyzing Markov Chains Using Kronecker Products: Theory and Applications

Dayar, Tugrul

Paperback

Series: Springerbriefs in Mathematics

ApplicationsGeneral MathematicsProbability & Statistics

ISBN10: 1461441897
ISBN13: 9781461441892
Publisher: Springer Nature
Published: Jul 24 2012
Pages: 86
Weight: 0.33
Height: 0.20 Width: 6.14 Depth: 9.21
Language: English
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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