Parameter Estimation I

4 credits

Linear least Squares (batch, recursive, total, sparse, pseudoinverse, QR, SVD); Statistical figures of merit (bias, consistency, Cramer-Rao lower-bound, efficiency); Maximum likelihood estimation (MLE); Sufficient statistics; Algorithms for computing the MLE including the Expectation Maximation (EM) algorithm. The problem of missing information; the problem of outliers. Recommended preparation: ECE 109 and ECE 153. Prerequisites: graduate standing.