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:

1.Multivariate normal distribution.

2. Principal component analysis.

3. Linear regression.

4. Nonlinear regression.

5. Bayes theorem. Bayesian parameters estimates. Bayesian inference in linear model.

6. Time series and their frequency domain description. Kalman-Bucy filtr.

Syllabus of tutorials:

No exercises.

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: