Quantitative Model Checking
Master formal verification: Learn to model and analyze probabilistic systems using Markov chains and temporal logics.
Master formal verification: Learn to model and analyze probabilistic systems using Markov chains and temporal logics.
This course introduces quantitative model checking for Markov chains, a powerful technique for verifying probabilistic systems. It covers the fundamentals of computational tree logic (CTL), discrete-time and continuous-time Markov chains, and their respective temporal logics: PCTL and CSL. Students will learn to model complex systems, express dependability properties, and use algorithms to verify these properties. The course emphasizes both theoretical understanding and practical application, teaching students to analyze and compute satisfaction sets for various properties in probabilistic systems.
4.2
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What you'll learn
Understand and apply Computational Tree Logic (CTL) for modeling system properties
Master the concepts of Discrete-Time and Continuous-Time Markov Chains
Learn to express and verify probabilistic properties using PCTL and CSL
Develop skills in model checking algorithms for various temporal logic formulas
Analyze the temporal evolution of Markov chains and compute transient probabilities
Understand state classification and compute steady-state probabilities
Skills you'll gain
This course includes:
4 Hours PreRecorded video
27 assignments
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There are 5 modules in this course
This course provides a comprehensive introduction to quantitative model checking for Markov chains. It is structured into five modules, covering Computational Tree Logic (CTL), Discrete-Time Markov Chains (DTMCs), Probabilistic CTL (PCTL), Continuous-Time Markov Chains (CTMCs), and Continuous Stochastic Logic (CSL). The curriculum progresses from basic concepts to advanced techniques, teaching students how to model complex systems, express dependability properties, and verify these properties using sophisticated algorithms. Throughout the course, students will learn to analyze the temporal evolution of Markov chains, compute transient and steady-state probabilities, and apply uniformization techniques for efficient model checking. By the end of the course, students will be equipped with the skills to formally verify probabilistic systems in various domains, including embedded systems, cyber-physical systems, and communication protocols.
Computational Tree Logic
Module 1 · 3 Hours to complete
Discrete Time Markov Chains
Module 2 · 3 Hours to complete
Probabilistic Computational Tree Logic
Module 3 · 2 Hours to complete
Continuous Time Markov Chains
Module 4 · 2 Hours to complete
Continuous Stochastic Logic
Module 5 · 2 Hours to complete
Fee Structure
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Instructor
Expert in Safety-Critical Systems and Cyber-Physical Infrastructures
Since October 2014, I have served as a professor in the Safety-Critical Systems group within the Faculty of Mathematics and Computer Science at Westfälische Wilhelms-Universität Münster. I am also affiliated with the Design and Analysis of Communication Systems group at the University of Twente, where I transitioned from assistant professor (2010-2016) to associate professor in March 2016. I earned my Ph.D. in Computer Science from the University of Twente in 2008 and my M.Sc. from RWTH Aachen in 2004. My research primarily focuses on the dependability and security of critical 24/7 infrastructures, including electrical power systems and telecommunications. I am particularly interested in evaluating charging and discharging strategies for local energy storage in smart homes and ensuring the security of control networks, such as SCADA, within the context of smart grids.
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4.2 course rating
52 ratings
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