Description

Probability theory and stochastic models. Probability and random variables, probability distributions and densities. Conditional probability and densities. Functions of random variable. Expectations and moments of random variables Conditional expectations. Gaussian random vectors, linear operators of Gaussian random variables. Estimation with static linear Gaussian system models. Markov chains. Stochastic process and linear dynamic system models. Error analysis and computer related problems. Numerical Methods. Numerical methods in matrix algebra. Curve fitting. Optimization techniques.

Program

M.Sc. in Electrical and Control Engineering

Objectives

• The student should become acquainted with: various methodologies for solving mathematical problems related to stochastic processes, numerical methods, curve fitting and optimization.