Input Modeling with PhaseType Distributions and Markov Models : Theory and Applications
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The work Input Modeling with PhaseType Distributions and Markov Models : Theory and Applications represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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Input Modeling with PhaseType Distributions and Markov Models : Theory and Applications
Resource Information
The work Input Modeling with PhaseType Distributions and Markov Models : Theory and Applications represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Input Modeling with PhaseType Distributions and Markov Models : Theory and Applications
 Title remainder
 Theory and Applications
 Statement of responsibility
 by Peter Buchholz, Jan Kriege, Iryna Felko
 Subject

 Mathematical Modeling and Industrial Mathematics
 Probability Theory and Stochastic Processes
 Computer software
 Mathematics
 Distribution (Probability theory)
 Mathematical Applications in Computer Science
 Electronic resources
 Computer software
 Mathematics
 Computer software
 Mathematical Software
 Distribution (Probability theory)
 Distribution (Probability theory)
 Mathematics
 Language
 eng
 Summary
 Containing a summary of several recent results on Markovbased 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 uptodate 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. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the interarrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and nonlinear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published
 Image bit depth
 0
 LC call number

 QA273.A1274.9
 QA274274.9
 Literary form
 non fiction
 Series statement
 SpringerBriefs in Mathematics,
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