К содержимому
learnspaceYOUR NEXT CHAPTER
ПРОСТРАНСТВО ОБУЧЕНИЯ
ГлавнаяКаталог курсовМоё обучениеCoursera

Знания без границ

Учитесь у лучших университетов и компаний мира.

Открыть Coursera
Интеграция
Пространство университета
Моё пространствоСтраница курса
↵
ЯЛичный кабинетСтудент
© 2026 LearnSpaceКаждый день — возможность узнать больше.Помощь
Quantitative Model Checking · LearnSpace
Назад в каталог
courseraПрограммирование

Quantitative Model Checking

Курс от 28DIGITAL
Средний≈ 13.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Welcome to the cutting-edge course on Quantitative Model Checking for Markov Chains! As technology permeates every aspect of modern life—Embedded Systems, Cyber-Physical Systems, Communication Protocols, and Transportation Systems—the need for dependable software is at an all-time high. One tiny flaw can lead to catastrophic failures and enormous costs. That's where you come in. The course kicks off with creating a State Transition System, the basic model that captures the intricate dynamics of real-world systems. Soon you'll step into the world of Discrete-time and Continuous-time Markov Chains—powerful mathematical formalisms that are versatile enough to model complex systems yet elegant in their design. These aren't just theories; they are tools actively used across various domains for performance and dependability evaluation. But we won't stop at modelling. The heart of this course is 'Model Checking,' a formal verification method that scrutinizes the functionality of your system model. Learn how to express dependability properties, track the evolution of Markov chains over time, and verify whether states meet particular conditions—all using advanced computational algorithms. By the end of this course, you'll be equipped with the skills to: - Specify dependability properties for a range of transition systems. - Understand the temporal evolution of Markov chains. - Analyze and compute the satisfaction set for multiple properties. Are you ready to become an expert in ensuring the reliability of tomorrow's technologies? Click here to Enroll today and join us in mastering the art and science of model checking.

Навыки, которые вы освоите

Markov ModelComputational LogicProbabilityMathematical ModelingAlgorithmsTheoretical Computer ScienceSystems AnalysisProbability DistributionApplied MathematicsVerification And ValidationSoftware TestingProcess ModelingStatistical Modeling

Программа курса

5 модулей · 65 учебных материалов

01Module 1: Computational Tree Logic13 материалов

Lecture 1: Introduction

Welcome!ВидеоIntroductionВидеоScript 1 and 2.1ЧтениеFormulate for yourselfЗадание

Учитесь у экспертов

Anne Remke

Prof. dr.

Quantitative Model Checking
В каталоге вашей программы

Инвестируйте в себя

Новые знания — в удобное для вас время.

Начать на Coursera

Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 13.7 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Часть программы вашего университета

Lecture 2: Semantics of CTL

Semantics of CTLВидео
Script 2.2 and 2.3Чтение

Lecture 3: Model Checking CTL

Test your understanding of CTL semanticsЗаданиеModel Checking CTLВидео

Lecture 4: The Until Operator

The Until OperatorВидеоCheck your understanding of CTLЗадание

Lecture 5: The Always Operator

The Always OperatorВидеоScript 2.4ЧтениеModel checking eventually, always and untilЗадание
02Discrete Time Markov Chains12 материалов

Lecture 1: Introduction to DTMCs

Introduction to DTMCsВидеоScript 3.1 and 3.2Чтение

Lecture 2: Evolution in Time

Evolution in TimeВидеоEvolution of DTMCsЗадание

Lecture 3: Transient probabilities

Transient probabilitiesВидеоCompute transient probabilitiesЗадание

Lecture 4: State classification

State classificationВидеоScript 3.3ЧтениеClassification of DTMC states True or False?Задание

Lecture 5: Steady-state probabilities

Steady-state probabilitiesВидеоState classificationЗаданиеSteady-state computationЗадание
03Probabilistic Computational Tree Logic14 материалов

Lecture 1: Syntax of PCTL

Syntax of PCTLВидеоPCTL SyntaxЗадание

Lecture 2: Model checking and the Next operator

Model checking and the Next operatorВидеоChecking PCTL nextЗаданиеScript: 4.1 and 4.2Чтение

Lecture 3: Time-bounded Until

Time-bounded UntilВидеоTest your understanding of PCTL UntilЗадание

Lecture 4: Backwards computation

Backwards computationВидеоChecking time-bounded untilЗаданиеScript: 4.3.1 and 4.3.2Чтение

Lecture 5: Unbounded Until

Unbounded UntilВидеоChecking unbounded untilЗаданиеScript 4.3.3ЧтениеTest your understanding of PCTLЗадание
04Continuous Time Markov Chains13 материалов

Lecture 1: Definition of a CTMC

Definition of a CTMCВидеоScript: 5.1 and 5.2Чтение

Lecture 2: Generator matrix

Generator matrixВидеоGenerator matrixЗаданиеTest your understanding of CTMCsЗадание

Lecture 3: Steady-state probabilities

Steady-state probabilitiesВидеоSteady state probability in CTMCsЗаданиеIdentifying BSCCsЗадание

Lecture 4: Example - Triple Modular Redundancy

Triple Modular RedundancyВидеоScript: 5.3Чтение

Lecture 5: Uniformization

UniformisationВидеоTest your understanding of UniformisationЗаданиеUniformisationЗадание
05Continuous Stochastic Logic13 материалов

Lecture 1: Syntax and semantics

Model checking CSLВидеоAssembly lineЗадание

Lecture 2: Time-bounded next

Model checking and Time-bounded nextВидеоScript: 6.1ЧтениеTest your understanding of CSL (I)Задание

Lecture 3: Steady-state

Model checking the steady-state operatorВидеоSteady state and nextЗаданиеTest your understanding of CSL (II)Задание

Lecture 4: Time-bounded Until

Script: 6.2ЧтениеTime-bounded UntilВидеоTime bounded until in CSLЗаданиеTest your understanding of CSL (III)Задание

Lecture 5: An application

An applicationВидео