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Stochastic Model Checking

International Autumn School, ROCKS 2012, Vahrn, Italy, October 22-26, 2012. Advanced Lectures

  • Textbook
  • © 2014

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Editors:
  1. Anne Remke

    1. Design and Analysis of Communication Systems, University of Twente, The Netherlands

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  2. Mariëlle Stoelinga

    1. Department of Computer Science, University of Twente, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

  • Tutorial lectures given by leading scientists
  • Up-to-date results
  • State-of-the-art report

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8453)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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About this book

The use of stochastic models in computer science is wide spread, for instance in performance modeling, analysis of randomized algorithms and communication protocols which form the structure of the Internet. Stochastic model checking is an important field in stochastic analysis. It has rapidly gained popularity, due to its powerful and systematic methods to model and analyze stochastic systems. This book presents 7 tutorial lectures given by leading scientists at the ROCKS Autumn School on Stochastic Model Checking, held in Vahrn, Italy, in October 2012. The 7 chapters of this tutorial went through two rounds of reviewing and improvement and are summarizing the state-of-the-art in the field, centered around the tree areas of stochastic models, abstraction techniques and stochastic model checking.

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Table of contents (7 chapters)

  1. Front Matter

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  2. Analyzing Oscillatory Behavior with Formal Methods

    • Alexander Andreychenko, Thilo Krüger, David Spieler
    Pages 1-25

  3. A Tutorial on Interactive Markov Chains

    • Florian Arnold, Daniel Gebler, Dennis Guck, Hassan Hatefi
    Pages 26-66

  4. A Theory for the Semantics of Stochastic and Non-deterministic Continuous Systems

    • Carlos E. Budde, Pedro R. D’Argenio, Pedro Sánchez Terraf, Nicolás Wolovick
    Pages 67-86

  5. On Abstraction of Probabilistic Systems

    • Christian Dehnert, Daniel Gebler, Michele Volpato, David N. Jansen
    Pages 87-116

  6. Computing Behavioral Relations for Probabilistic Concurrent Systems

    • Daniel Gebler, Vahid Hashemi, Andrea Turrini
    Pages 117-155

  7. Markov Reward Models and Markov Decision Processes in Discrete and Continuous Time: Performance Evaluation and Optimization

    • Alexander Gouberman, Markus Siegle
    Pages 156-241

  8. Applying Mean-Field Approximation to Continuous Time Markov Chains

    • Anna Kolesnichenko, Valerio Senni, Alireza Pourranjabar, Anne Remke
    Pages 242-280

  9. Back Matter

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Editors and Affiliations

  • Design and Analysis of Communication Systems, University of Twente, The Netherlands

    Anne Remke

  • Department of Computer Science, University of Twente, The Netherlands

    Mariëlle Stoelinga

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