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Mathematical Models and their Applications

Course Code:
11MMJ
Academic Degree:
master
Study Programme:
Air Traffic Control and Management (N1041A040010)
semesterlanguage
1czech flag
Erasmus:
course for Erasmus and Exchange students in winter semester
Form of Study:
full-time and part-time
Credits:
4
Number of Hours:
2 + 2 hours per week - in full-time study
12 hours per semester - in part-time study
Type of Course:
obligatory
Course Completion:
credit, exam
Course Tutor:
 
roh  Lectures:
doc. Ing. Evženie Uglickich, CSc.
roh  Training Course:
doc. Ing. Michal Matowicki, Ph.D.
roh  Part-time Study:
Ing. Pavla Pecherková, Ph.D.
Supervising Department:
Department of Applied Mathematics (16111)
Keywords:
Bayesian parameter estimation, output prediction, state estimationess, regression model, state space model, estimation, application of dynamic models.
Abstract:
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.
Objectives:
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.