Point Processes

WiSe 2022/2023



Monday 10-12, SRZ 204
Thursday 10-12, SRZ 204


Wednesday 14-16, SR 1D
Lecturer: JProf. Dr. Anna Gusakova
Assistent: Jonas Jalowy




This lecture is addressed to master students, who are familiar with stochastics and probability theory. The course can be taken as part of one of the modules Probability Theory and its Applications or Stochastic Processes.

Course syllabus:

In this course we will study general point processes and their basic properties. A point process is a stochastic object, which is formally defined as a random counting measure, but in applications point processes are often identified with their support and viewed as a random set of points. In a Poisson point process the points are independent and we will study this fundamental example in great detail. We shall also discuss some examples with interesting dependent, repulsive particles such as determinantal point processes.

Point processes play an important role in many modern areas of probability theory, e.g. as the object of study in random matrix theory (the eigenvalue distribution) and as the generating object for models in stochastic geometry and random graphs. At the end of the course we will get to know some geometrical models based on the Poisson point process, like random tessellations and the Boolean model.


The participants are kindly asked to register for the course in the learnweb in order to access the material of the class.

Course assessment: Successful completion of 50% of the homework sets as well as an oral exam at the end of the course