Stochastic Processes
Instructor: Dr. Alexander Kalinin
Schedule and Venue
| Events | Date/Time | Room |
|---|---|---|
| Lectures | Tuesday, 14:15 - 15:45 Thursday, 8:30 - 10:00 | B004 |
| Exercise Classes | Wednesday, 14:15 - 15:45 | B004 |
| Additional Exercise Classes | Wednesday, 12:00 - 12:45 | B045 |
The lectures and all exercise classes are conducted by Dr. Alexander Kalinin, and the course is administered on Moodle, where interested students are requested to register.
This course provides a rigorous foundation in the advanced theory of stochastic processes featuring Polish state spaces and arbitrary parameter sets. The lectures are devoted to the general construction of stochastic processes, encompassing a systematic analysis of their sample path regularity and distributional properties. In particular, we will focus on Gaussian fields and Lévy processes and derive efficient simulation schemes employing generative artificial intelligence.
- Bauer, H.: Probability theory, De Gruyter, 2011.
- Marcus, M. B. and Rosen, J.: Markov processes, Gaussian processes, and local times, Cambridge University Press, 2006.
- Applebaum, D.: Lévy processes and stochastic calculus, Cambridge University Press, 2009.
All three books are available as PDF files for LMU students at the university library.
Target Participants: Master students in Mathematics and Financial and Insurance Mathematics.
Pre-requisites: Probability theory and measure and integration theory.
Applicable credits: 9 ECTS. Students may apply the credits from this course to the
- Master in Mathematics, PO 2021 (WP 4),
- Master in Financial and Insurance Mathematics, PO 2021 (WP 12), PO 2019 (WP 13).