Students are able to model and analyse univariate and multivariate statistical data with temporal and spatial dependencies.

Analysis of different (multivariate) data sets using the methods presented in the lectures and appropriate R-packages.

Multivariate distributions, conditional distributions, conditional expectation.Multivariate normal distribution and its properties.(Probabilistic) Principal Component Analysis.Copulas and applications thereof.Analysis of spatially dependent data.Analysis of temporally dependent data.

Stochastics 2Statistical MethodsData AnalysisMathematical StatisticsComputational Statistics

A 15 min presentation on the results of the project Can - optionally - be delivered online.Date of presentation by personal agreement.

Studentsare free to choose whether they do a project for the first part (Prof. Pilz, spatial models) or the second part (Prof. Borovcnik, temporal models).A 15-min presentation of the results of the project, is followed by an oral examination about the underlying concepts.The common core (labelled as such) is in any case part of the questions for the lecture.

The project quality and the presentation contribute to the exercises grading.