Mathematical Models and their Applications

Garant předmětu:

Evženie Uglickich

Lecturer:

Michal Matowicki, Ivan Nagy, Pavla Pecherková, Evženie Uglickich

Tutor:

Michal Matowicki, Ivan Nagy, Pavla Pecherková, Evženie Uglickich

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:

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:

• PL nav.prez.22/23 (pro program PL) (compulsory course)
• PL nav.kombi 22/23 (pro program PL) (compulsory course)
• PL nav.prez.23/24 (pro program PL) (compulsory course)
• PL nav.kombi 23/24 (pro program PL) (compulsory course)
• PL nav.prez.24/25 (pro program PL) (compulsory course)
• PL nav.kombi 24/25 (pro program PL) (compulsory course)