470-2406/01 – Models with Uncertainty (MN)

Gurantor departmentDepartment of Applied MathematicsCredits4
Subject guarantorIng. Jan Kracík, Ph.D.Subject version guarantorIng. Jan Kracík, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Year3Semestersummer
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
KRA0220 Ing. Jan Kracík, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 0+2
Part-time Graded credit 0+8

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:

JAYNES, Edwin T., BRETTHORST, G. Larry, ed. Probability theory: the logic of science. Cambridge: Cambridge University Press, 2003. ISBN 0-521-59271-2. ROBERT, Christian P. a George. CASELLA. Monte Carlo statistical methods. 2nd ed. New York: Springer, c2004. ISBN 0-387-21239-6.

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

Part-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100  51
Mandatory attendence parzicipation: Obligatory participation at all excercises, 2 apologies are accepted

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (B0541A170008) Computational and Applied Mathematics P Czech Ostrava 3 Compulsory study plan
2020/2021 (B0541A170008) Computational and Applied Mathematics K Czech Ostrava 3 Compulsory study plan
2019/2020 (B0541A170008) Computational and Applied Mathematics P Czech Ostrava 3 Compulsory study plan
2019/2020 (B0541A170008) Computational and Applied Mathematics K Czech Ostrava 3 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner