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

Mohinder S. Grewal, Angus P. Andrews (2015.), Kalman Filtering, John Wiley & Sons
Athanasios Papoulis (1991.), Probability, Random Variables, and Stochastic Processes, McGraw-Hill Science, Engineering & Mathematics
Frank Beichelt (2006.), Solutions Manual for Stochastic Processes in Science, Engineering And Finance, Chapman & Hall

For students

General

ID 154760
  Summer semester
6 ECTS
L0 English Level