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Input Modeling with Phase-Type Distributions and Markov Models: Theory and Applications (SpringerBriefs in Mathematics)

Input Modeling with Phase-Type Distributions and Markov Models: Theory and Applications (SpringerBriefs in Mathematics) by Peter Buchholz
English | 30 Jun. 2014 | ISBN: 3319066730 | 140 Pages | EPUB | 2 MB
Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area.

Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models.

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