Description

This graduate textbook provides an alternative to discrete event simulation. It describes how to formulate discrete event systems, how to convert them into Markov chains, and how to calculate their transient and equilibrium probabilities. The most appropriate methods for finding these probabilities are described in some detail, and templates for efficient algorithms are provided. These algorithms can be executed on any laptop, even in cases where the Markov chain has hundreds of thousands of states. This book features the probabilistic interpretation of Gaussian elimination, a concept that unifies many of the topics covered, such as embedded Markov chains and matrix analytic methods.

The material provided should aid practitioners significantly to solve their problems. This book also provides an interesting approach to teaching courses of stochastic processes.


Numerical Methods for Solving Discrete Event Systems: With Applications to Queueing Systems

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Hardback by Winfried Grassmann , Javad Tavakoli

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This graduate textbook provides an alternative to discrete event simulation. It describes how to formulate discrete event systems, how to... Read more

    Publisher: Springer International Publishing AG
    Publication Date: 05/11/2022
    ISBN13: 9783031100819, 978-3031100819
    ISBN10: 3031100816

    Number of Pages: 362

    Non Fiction , Mathematics & Science , Education

    Description

    This graduate textbook provides an alternative to discrete event simulation. It describes how to formulate discrete event systems, how to convert them into Markov chains, and how to calculate their transient and equilibrium probabilities. The most appropriate methods for finding these probabilities are described in some detail, and templates for efficient algorithms are provided. These algorithms can be executed on any laptop, even in cases where the Markov chain has hundreds of thousands of states. This book features the probabilistic interpretation of Gaussian elimination, a concept that unifies many of the topics covered, such as embedded Markov chains and matrix analytic methods.

    The material provided should aid practitioners significantly to solve their problems. This book also provides an interesting approach to teaching courses of stochastic processes.


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