Model-Based Estimation and Signal Analysis

Abstract

The course develops a selection of topics pivoting around state space models, factor graphs, and pertinent algorithms for estimation, model fitting, and learning.

Objective

The course develops a selection of topics pivoting around state space methods, factor graphs, and pertinent algorithms: - hidden-Markov models - factor graphs and message passing algorithms - linear state space models, Kalman filtering, and recursive least squares - Gibbs sampling, particle filter - recursive local polynomial fitting for signal analysis - parameter learning by expectation maximization - linear-model fitting beyond least squares: sparsity, Lp-fitting and regularization, jumps - binary, M-level, and half-plane constraints in control and communications

Resources