Stochastic Processes SS23
Abstract: Diffusing particles, stochastic gene expression in biological cells, or noisy biological clocks 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:
- MSc course 'Physics of Life'
- Physics students at the Bachelor or Master’s level
- Mathematics students interested in applications of stochastic processes
- 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
Scope: lecture: 2 hours/week; tutorials: 2 hours/ 2 weeks
Time:
Lecture every Thursday 9:20am (first lecture on April 6th)
Tutorial right after every Thursday 11:00am
Location for lecture and tutorial:
Biotec E06 until May 11th
http://navigator.tu-dresden.de/etplan/biz/00/raum/062100.0060
CRTD Auditorium left thereafter
http://navigator.tu-dresden.de/raum/062400.1840
(Note: renovations at Biotec/CRTD; please check this site regularly)
Audience: Master students 'Physics of Life'; Bachelor and Master Physics students; students from the CMCB-Master 'NanoBioPhysics'; Master students from the 'Computational Modeling and Simulation' course
Specialization area: Soft Condensed Matter and Biological Physics
Previous knowledge: Multi-variate calculus, Thermodynamics and/or first course in Statistical Mechanics
Lectures:
Benjamin Friedrich (TU Dresden)
Tutorial:
Julian Rode (TU Dresden)