Markov Processes

Tuesday 10:00-12:00, SRZ 205
Thursday 8:00-10:00, SRZ 205

Lecture

Tutorials

Markov Processes are central objects in probability theory. Its characterising property can be characterised intuitively as follows: given the information of the historical development the distribution of the future development depends solely on the current position of the process. We will provide standard techniques for th definition and analysis of such processes.

An important tool for defining Markov processes in discrete spaces will be Poisson point processes. In particular, these can be used to define in time and space homogeneous Markov processes in R^d, so called Lévy processes. Moreover, we provide techniques that allow to define general Markov processes with continuous state space (theorem of Hille-Yosida and the martingale problem).

Lecture notes will be provided
"Markov Processes, Brownian Motion, and Time Symmetry " by Kai Lai Chung and John B. Walsh
"Applied Probability and Queus" by Soren Asmussen

Please use the learnweb to access the material of the class.

t.b.a.