Found 7 relevant results in 1.06s where lecturer="Fadoua Balabdaoui"
The course reviews the main concepts in statistical learning including statistical models, parametric and non-parametric inference, hypothesis testing and p-values, and classification. Additionally, it serves as a gentle introduction into some special topics such as the bootstrap, the EM-algorithm, causal inference and kernel estimation.
The course will cover: Random experiments, sample spaces, events| Probability measures| Conditional probability, Bayes rule.| Independence| Random variables (discrete & continuous) and their distributionfunction, Well-known distributions| Joint distributions| Expectation and variance| Covariance and correlation| Conditional expectation,Modes of convergence.
This course is a review of the main results in decision theory.
Probability and Statistics
Probability and Statistics
Wahrscheinlichkeit und Statistik
- Diskrete Wahrscheinlichkeitsräume- Stetige Modelle- Grenzwertsätze- Einführung in die Statistik
Review of some non-standard regression models and the statistical properties of estimation methods in such models.
The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ACVF and ACF, Estimation of trend and seasonal component, Linear processes, ARMA processes, Forecasting and estimation of a missing value, the Innovation Algorithm.