Quantitative Model Checking(으)로 돌아가기

4.2

27개의 평가

•

5개의 리뷰

The integration of ICT (information and communications technology) in different applications is rapidly increasing in e.g. Embedded and Cyber physical systems, Communication protocols and Transportation systems. Hence, their reliability and dependability increasingly depends on software. Defects can be fatal and extremely costly (with regards to mass-production of products and safety-critical systems).
First, a model of the real system has to be built. In the simplest case, the model reflects all possible states that the system can reach and all possible transitions between states in a (labelled) State Transition System. When adding probabilities and discrete time to the model, we are dealing with so-called Discrete-time Markov chains which in turn can be extended with continuous timing to Continuous-time Markov chains. Both formalisms have been used widely for modeling and performance and dependability evaluation of computer and communication systems in a wide variety of domains. These formalisms are well understood, mathematically attractive while at the same time flexible enough to model complex systems.
Model checking focuses on the qualitative evaluation of the model. As formal verification method, model checking analyzes
the functionality of the system model. A property that needs to be analyzed has to be specified in a logic with consistent syntax and semantics. For every state of the model, it is then checked whether the property is valid or not.
The main focus of this course is on quantitative model checking for Markov chains, for which we will discuss efficient computational algorithms. The learning objectives of this course are as follows:
- Express dependability properties for different kinds of transition systems .
- Compute the evolution over time for Markov chains.
- Check whether single states satisfy a certain formula and compute the satisfaction set for properties....

필터링 기준:

교육 기관: Carl H

•Feb 01, 2019

Lectures are rushed and not explained well. Discussion forms seem to be filled with "Is there inaccuracy in in quiz X". Here is a direct quote from one of the discussions "I actually didn't use the formula from the lecture but from the cited paper by Baier et. al. - same stuff. works for 11, doesn't for 12". Granted that the subject matter this course covers is difficult, I feel like this course makes it harder rather than easier. I wouldn't recommend it to anyone.

교육 기관: ElissaHu

•Apr 27, 2018

difficult in week4&week5, but interesting

교육 기관: Joseph V

•Jan 19, 2018

The lectures on Coursera are nice, but please remove to cringe parts (any outside shot video material).

Overall the course is very bad because the tele lectures are very bad quality and thus does not motivate you at all to keep track of the course during the period. Recommendation: Ditch tele lectures all together and give actual bonus points for completing Coursera parts on time.

교육 기관: Mario A G S

•Sep 16, 2017

Very good course!!!

교육 기관: 潘临杰

•Aug 25, 2017

模型检测入门教程，学完课程之后对于模型检测有了直观的认识。