Markov Prozesse

SoSe 2026

Information

Lecturer:  Prof. Dr. Steffen Dereich
Assistance:   
Lecture: Tuesdays 8-10 or 10-12 (to be decided in the first lecture) in SRZ 204
Thursday 16-18 in M4

First lecture: 14th of April at 10 in SRZ 204
Tutorials: Monday 16-18 in M4
KommVV:

Link
Content: In this lecture we analyse the class of Markov processes in continuous time. We start with a focus on processes processes in discrete state spaces. For this we introduce point processes as a tool for defining transition trajectories. To provide a unifying framework for general Markov processes, the discussion concludes with an introduction to semigroup theory, characterizing stochastic evolution through the lens of functional analysis and infinitesimal generators.
Literature:
  • Anderson, W. J. (1991). Continuous-Time Markov Chains: An Applications-Oriented Approach. New York: Springer-Verlag.
  • Ethier, S. N., & Kurtz, T. G. (1986). Markov Processes: Characterization and Convergence. New York: John Wiley & Sons.
  • Kolokoltsov, V. N. (2011). Markov Processes, Semigroups and Generators. Berlin: De Gruyter.
  • Liggett, T. M. (2010). Continuous Time Markov Processes: An Introduction. Providence, RI: American Mathematical Society.
Learnweb: Please register for the course in the Learnweb.
Admission to exam: