Mathematical Statistics II

Course guarantor:

Daniela Jarušková

Lecturer:

Daniela Jarušková

Tutor:

Supervisor:

Department of Mathematics

Synopsis:

Multivariate normal distribution. Principal component analysis. Linear regression. Nonlinear regression. Bayes theorem. Bayesian parameters estimates. Bayesian inference in linear model. Time series and their frequency domain description. Kalman-Bucy filtr.

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Requirements:

Basic statistical course of statistics either in their MGr studies or in the course Mathematical statistics I.

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

To get a deeper knowledge that is of interest for students, especially for their future disertation thesis.

Study materials:

Jay Devore: Probability and Statistics for Engineering and the Sciences.

Larry Wasserman: All of Statistics. A concise Course in Statistical Inference, Springer, 2005.

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: