| 期刊全称 | Analyzing Markov Chains using Kronecker Products | | 期刊简称 | Theory and Applicati | | 影响因子2023 | Tuğrul Dayar | | 视频video | | | 发行地址 | First to provide a solely Kronecker product based treatment of Markov chain analysis.The subject matter is interdisciplinary and at the intersection of applied mathematics, specifically numerical line | | 学科分类 | SpringerBriefs in Mathematics | | 图书封面 |  | | 影响因子 | 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. | | Pindex | Book 2012 |
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发表于 2025-3-21 19:35:41
