Statistical Signal Processing

Lecturer: Wolfgang Utschick, Dominik Semmler

Target Audience: Master EI and MSCE

Language: English

Next Exam: tbd. (no responsibility is taken for the correctness of this information)

Additional Information: TUMonline and Moodle

Lectures/Tutorials in Summer Semester 2024

Contents

Probability and stochastic processes: fundamentals revisited

Parameter estimation: statistical modeling, maximum likelihood estimation, Bayesian estimation, asymptotic optimality

Minimum mean squared error estimation: conditional mean estimation and MMSE, linear MMSE estimation, orthogonality principle, Wiener filtering

Recursive estimation of stochastic processes: Kalman filtering, particle filtering

Hypotheses testing: statistical model, Neyman-Pearson test, maximum-likelihood test, maximum-a-posteriori test, Bayesian test, risk functionals, sufficient statistics, asymptotic optimality

Selected topics and applications: confindence analysis, kernel methods, neural networks, etc.