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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024

Mathematical Models and their Applications

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Code Completion Credits Range Language
11MMJ Z,ZK 4 2P+2C+12B Czech
Garant předmětu:
Evženie Uglickich
Lecturer:
Michal Matowicki, Ivan Nagy, Pavla Pecherková, Evženie Uglickich, Šárka Voráčová
Tutor:
Michal Matowicki, Ivan Nagy, Pavla Pecherková, Evženie Uglickich, Šárka Voráčová
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 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-23
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