470-2406/01 – Models with Uncertainty (MN)
| Gurantor department | Department of Applied Mathematics | Credits | 4 |
| Subject guarantor | Ing. Jan Kracík, Ph.D. | Subject version guarantor | Ing. Jan Kracík, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 3 | Semester | summer |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
Students get acquainted with a probabilistic approach to uncertainty in real world models.
Teaching methods
Tutorials
Project work
Summary
Mathematical models of real world systems are often loaded with uncertainty caused by random input parameters, model imprecision, imprecise data, etc. Probability theory is often used for repreenting quantifying the uncertainty in the models.
Compulsory literature:
Recommended literature:
W.H. Press, B.P. Flannery, S.A. Teukolski, W.T. Vetterling, Numerical Recipes in C. Cambridge University Press, 1990.
W. E. Boyce, R. C. DiPrima: Elementary differential equations. Wiley, New York 1992
Way of continuous check of knowledge in the course of semester
semestral project
E-learning
Other requirements
There are not defined other requirements for student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Static models with random inputs
Monte Carlo methods
Linear dynamical models with Gaussian noise
Kalman filter
Bayesian approach to inverse problems
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction