Mathematical Statistics II

The course is not on the list Without time-table

Code	Completion	Credits	Range	Language
D01MS2	ZK		2P	Czech

Course guarantor:

Lecturer:

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.

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:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: