Lecture: Markov Chains
Lecture: Tuesday, 14-16h, SR 127, Ernst-Zermelo-Str. 1.
Exercise class: 2 hours, date to be determined and announced in the first lecture.
News
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Description
The class of Markov chains is an important class of (discrete-time) stochastic processes that are used frequently to model for example the spread of infections, queuing systems or switches of economic scenarios. Their main characteristic is the Markov property, which roughly means that the future depends on the past only through the current state. In this lecture we provide the mathematical foundation of the theory of Markov chains. In particular, we learn about path properties, such as recurrence and transience, state classifications and discuss convergence to the equilibrium. We also study extensions to continuous time. On the way we discuss applications to biology, queuing systems and resource management. If the time allows, we also take a look at Markov chains with random transition probabilities, so-called random walks in random environment, which is a prominent model in the field of random media.
Prior knowledge
Required prior knowledge: Stochastics I
Useful prior knowledge: Analysis III, Probability Theory I
Literature
TBA
Lecturers
Usability
- Elective (Option Area) (2HfB21)
- Compulsory Elective in Mathematics (BSc21)
- Supplementary Module in Mathematics (MEd18)
- Applied Mathematics (MSc14)
- Mathematics (MSc14)
- Specialisation Module (MSc14)
- Elective (MSc14)
- Elective in Data (MScData24)
Please refer to the supplements to the module handbooks supplements to the module handbooks for the number of ECTS credits.