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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Pages323-341
Number of pages19
ISBN (Electronic)978-3-319-23820-3
ISBN (Print)978-3-319-23819-7
DOIs
StatePublished - 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9333
ISSN (Print)0302-9743

Abstract

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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