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Machine Learning Methods in Statistical Model Checking and System Design – Tutorial

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Original languageEnglish
Title of host publicationRuntime Verification
Subtitle of host publication6th International Conference, RV 2015, Vienna, Austria, September 22-25, 2015. Proceedings
EditorsEzio Bartocci, Rupak Majumdar
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages19
ISBN (Electronic)978-3-319-23820-3
ISBN (Print)978-3-319-23819-7
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Recent research has seen an increasingly fertile convergence of ideas from machine learning and formal modelling. Here we review some recently introduced methodologies for model checking and system design/parameter synthesis for logical properties against stochastic dynamical models. The crucial insight is a regularity result which states that the satisfaction probability of a logical formula is a smooth function of the parameters of a CTMC. This enables us to select an appropriate class of functional priors for Bayesian model checking and system design. We give a tutorial introduction to the statistical concepts, as well as an illustrative case study which demonstrates the usage of a newly-released software tool, U-check, which implements these methodologies.

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