Stochastic Processes and Filters

Course Description

Stationary processes. Stochastic integral. Ergodic theorems. Brownian motion. White noise. Martingals. Stopping times. Stochastic diffusion. Stochastic calculus. Ito s integral. Deterministic and stochastic dynamic systems. Prediction and filtering. Wiener filter. Kalman filter. Optimal smoothers. Nonlinear filtering.

Study Programmes

Postgraduate doctoral study programme

Literature

(.), Moohinder S. Grewal., Angus P. Andrews: Kalman filtering (theory and practice using MATLAB),
(.), A. Papoulis: Probability, random variables and stochastic processes, McGraw-Hill, Third Edition,
(.), F. Beichelt: Stochastic Processes in Science, Engineering and Finance, Chapman & Hall, 2006.,

General

ID 154760
  Summer semester
6 ECTS