Students acquire the fundamental background on Statistical Inference, including hypothesis testing, signal detection, parameter estimation, and optimal MMSE linear filtering (in particular, Wiener and Kalman filters). This is one of the foundations of modern Electrical Engineering in the ``Systems’ areas such as Automatic Controls, Communications, and Signal Processing.
Content
Hypothesis Testing (maximum a-posteriori probability, Neyman-Pearson testing, minimax testing) Signal Detection: uncoded modulation, coded modulation, ISI channels, Viterbi Algorithm, BCJR algorithm Parameter estimation: Fisher information and Cramer-Rao Bound, minimum variance unbiased estimation, Maximum-Likelihood estimation, Least-Squares, Bayesian estimation Bayesian inference, sum-product algorithm, and belief propagation The Approximated Message Passing (AMP) algorithm Wiener filters and Kalman filters.
Credits: 6LP
- Trainer/in: Giuseppe Caire
- Trainer/in: Tom Szollmann
- Trainer/in: Cagkan Yapar