Validation of Stochastic Systems: A Guide to Current Research / Edition 1

Validation of Stochastic Systems: A Guide to Current Research / Edition 1

by Christel Baier
     
 

ISBN-10: 3540222650

ISBN-13: 9783540222651

Pub. Date: 08/11/2004

Publisher: Springer Berlin Heidelberg

This tutorial volume presents a coherent and well-balanced introduction to the validation of shastic systems; it is based on a GI/Dagstuhl research seminar. Supervised by the seminar organizers and volume editors, established researchers in the area as well as graduate students put together a collection of articles competently covering all relevant issues in the

Overview

This tutorial volume presents a coherent and well-balanced introduction to the validation of shastic systems; it is based on a GI/Dagstuhl research seminar. Supervised by the seminar organizers and volume editors, established researchers in the area as well as graduate students put together a collection of articles competently covering all relevant issues in the area.

The lectures are organized in topical sections on: modeling shastic systems, model checking of shastic systems, representing large state spaces, deductive verification of shastic systems.

Product Details

ISBN-13:
9783540222651
Publisher:
Springer Berlin Heidelberg
Publication date:
08/11/2004
Series:
Lecture Notes in Computer Science Series, #2925
Edition description:
2004
Pages:
470
Product dimensions:
6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

Modelling Stochastic Systems.- Probabilistic Automata: System Types, Parallel Composition and Comparison.- Tutte le Algebre Insieme: Concepts, Discussions and Relations of Stochastic Process Algebras with General Distributions.- An Overview of Probabilistic Process Algebras and Their Equivalences.- Model Checking of Stochastic Systems.- Verifying Qualitative Properties of Probabilistic Programs.- On Probabilistic Computation Tree Logic.- Model Checking for Probabilistic Timed Systems.- Representing Large State Spaces.- Serial Disk-Based Analysis of Large Stochastic Models.- Kronecker Based Matrix Representations for Large Markov Models.- Symbolic Representations and Analysis of Large Probabilistic Systems.- Probabilistic Methods in State Space Analysis.- Deductive Verification of Stochastic Systems.- Analysing Randomized Distributed Algorithms.- An Abstraction Framework for Mixed Non-deterministic and Probabilistic Systems.- The Verification of Probabilistic Lossy Channel Systems.

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