Data Analysis and Stochastic Modelling

Winter Semester 2023/2024

The course takes place on Thursdays starting at 12:30 in room 28.2.123. It consists of 2SWS Lectures and 2SWS with practical programming exercises (1 SWS for Bachelor courses). The execises take place on Thursdays at 16:00 in room 28.0.087.

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Chapter 1: Predictions (HTML, 6 KB)
Chapter 2: Brownian Motion (HTML, 8 KB)
Chapter 3: Autocorrelations (HTML, 8 KB)
Chapter 4: Chaos (HTML, 9 KB)
Chapter 5: Non-Gaussianity (HTML, 15 KB)
Chapter 6: Time Reversal Symmetry (HTML, 6 KB)
Chapter 7: Active Motion (HTML, 7 KB)
Chapter 8: Spectral Methods (HTML, 12 KB)
Chapter 9: Fluctuations on Multiple Timescales (HTML, 8 KB)
Chapter 10: Bayesian Statistics and Kalman Filter (HTML, 16 KB)
Chapter 11: Autoregressive Models (HTML, 10 KB)
Chapter 12: Supervised Learning (HTML, 9 KB)
Chapter 13: Causality (HTML, 7 KB)

Exercise 1: Random Walks (HTML, 4 KB)
Exercise 2: Pseudo-Brownian Motion (HTML, 4 KB)
Exercise 3: Financial Time Series (HTML, 3 KB)
Exercise 4: Climate in Potsdam (HTML, 3 KB)
Exercise 5: Politics (HTML, 4 KB)
Exercise 6: Avogadro Constant (HTML, 5 KB)
Exercise 7: Wind Speed (HTML, 4 KB)
Exercise 8: Sunspots (HTML, 5 KB)