Validation of Stochastic Systems A Guide to Current Research

This tutorial volume presents a coherent and well-balanced introduction to the validation of stochastic 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 stochastic systems, model checking of stochastic systems, representing large state spaces, deductive verification of stochastic systems.

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.