This is a postgraduate level lecture on estimation theory
Contents:
Probability and Random Variables: Probability and relative frequency, event space, events, elementary events, sigma algebra, Borel fields, probability axioms, random variables and random vectors, probability distribution and probability distriburgion density, probability distribution and - density of random vectors, multivariate distributions and densities, joint densities, relations between random vectors and variables, mapping of random variables and vectors, joint densities and conditional densities, induced densities, moments and expectations of random vectors and functions of random vectors, mean, correlation and covariance, Gaussian distributions, central limit theorem, conditional expectations of jointly normal random vectors, optimal estimation, conditional mean estimation, minimum variance estimation, Bayesian estimation
- Dozent/in: Otmar Loffeld