Scripts

 

An implementation of the models in the book is available as interactive notebooks. The scripts will help students to better understand the impact of parameters
on performance characteristics, will avoid common pitfalls in the implementation,
and provide means for numerical robust and efficient implementations for researchers in the domain.

Chapter 1: Introduction 

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Chapter 3: Stochastic Processes

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Chapter 5: Non-Markovian Systems

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Python Modules

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Module DiscreteTimeAnalysis: The module provides a class for finite discrete distributions which are utilized for discrete-time analysis. For example, discrete-time GI/GI/1 systems can be analyzed with functions of the module.


Module MarkovModelModule: A directed graph class for modeling continuous-time Markov chains (CTMCs) as StateTransitionGraph. This allows easy (visual) checking of Markov models, visualization as transition graph, and Markov simulation (e.g. for large models). The analysis allows symbolic computation of closed formulas, as well as efficient computation of numerical values for steady-state probabilities and probabilities in the transient phase.