Mathematical Models and their Applications

The course is not on the list Without time-table

Code	Completion	Credits	Range	Language
11MMJ	Z,ZK	4	2P+2C+12B	Czech

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

System. Regression, discrete and logistic models. Bayesian estimation of model parameters. Parameter estimation of normal regression, discrete and logistic models. Classification with logistic model. One-step and multi-step prediction with regression and discrete models. State model. State estimation. Kalman filter. Control with regression and discrete models.

Requirements:

basic knowledge of statistics

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Teach students advanced methods for analyzing the behavior of dynamical systems, including system identification and output prediction for continuous and discrete random variables based on Bayesian statistics.

Study materials:

William M. Bolstad: Introduction to Bayesian Statistics, 2nd Edition. Willey, ISBN-13: 978-0470141151

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

Master Full-Time PL from 2022/23 (compulsory course)
Master Part-Time PL from 2022/23 (compulsory course)
Master Full-Time PL from 2023/24 (compulsory course)
Master Part-Time PL from 2023/24 (compulsory course)
Master Full-Time PL from 2024/25 (compulsory course)
Master Part-Time PL from 2024/25 (compulsory course)