Stochastic Processes with Python

TU Dresden | Wintersemester 2021 / 2022 Stochastic Processes with Python

Abstract: Diffusing particles, stochastic gene expression in biological cells, but also fluctuating stock prices all represent examples of stochastic processes. In this lecture, we will simultaneously develop the necessary mathematical theory and visualize this theory using computer experiments. We will use interactive Python notebooks to study selected examples of application, thus enabling learning by doing. Topics include: basic probability theory, Langevin equations that allow to predict the time-evolution of stochastic systems, statistical testing and inference. We will also discuss the link between stochastic dynamics and statistical physics.

Target audience:
- Physics students at the Bachelor or Master’s level
- Mathematics students interested in applications of stochastic processes
- NanoBioPhysics and Molecular Bioengineering Master students with a background in quantitative methods

Requirements: Previous programming experience is not a requirement, but a plus; previous exposure to ordinary differential equations (ODE) is recommended

Date: Monday 9am (first lecture on October 11th)
Location: REC/C118/U (at the Physics faculty)
https://navigator.tu-dresden.de/etplan/rec/01/raum/217501.0040

Every second Monday (i.e. 11.10.2021, 25.10.2021, ...), we will have programming tutorials right after the lecture:
Every second Monday 11:10am in the PC pool REC/B113
https://navigator.tu-dresden.de/etplan/rec/01/raum/217801.0320

Corona news: According to current corona regulations, a total of 20 people is allowed in the lecture hall (3G rules apply). Depending on the number of registered students, we will decide whether the lecture will be held in person, hybrid form or online via zoom. Please watch this page for updated information.

3G Awareness
Please take note of the TU Dresden's 3G access rules once at the beginning of the semester by filling out the questionnaire, which is opened in the OPAL course https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/32452870149/CourseNode/1634093595125267008 of the Department of Mathematics and Natural Sciences via the button "Submit declaration".

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