PřF:M7111 Topics on mathematical modelli - Course Information
M7111 Topics on mathematical modelling
Faculty of ScienceAutumn 2023
- Extent and Intensity
- 2/0/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: k (colloquium).
- Teacher(s)
- Mgr. Ondřej Pokora, Ph.D. (lecturer)
- Guaranteed by
- doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Mon 18:00–19:50 M2,01021
- Prerequisites
- Prerequisites: theoretical knowledge and practise in the scope of undergraduate courses of probability, mathematical statistics, calculus and software R.
- Course Enrolment Limitations
- The course is offered to students of any study field.
- Course objectives
- Selected methods of stochastic modeling are presented and developed. New trends in mathematical modeling are introduced and reviewed. Each part is supplemented by a summary of applied mathematical procedures and implemented in R software.
- Learning outcomes
- After passing the course, the student will be able:
to define and interpret the basic notions used in the basic parts of mathematical modeling and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in basic fields of mathematical modeling;
to apply acquired pieces of knowledge for the solution of specific problems including problems of applicative character. - Syllabus
- Binomial, hypergeometric, Poisson and exponential probability distribution.
- Poisson process.
- Renewal processes.
- Pure-birth process (Yule process), pure-death process, birth-and-death process.
- Galton-Watson branching process.
- Random walk.
- Brownian motion (Wiener process).
- Brownian motion with drift, First Passage Time.
- Diffusion processes.
- Literature
- TUCKWELL, Henry C. Elementary applications of probability theory : with an introduction to stochastic differential equations. 2nd ed. London: Chapman and Hall, 1995, xv, 292. ISBN 0412576201. info
- Teaching methods
- Classes are in full-time form: lectures = 2 hours a week – lectures, problem solving, discussions.
- Assessment methods
- Discussions, homeworks and problem solving. To conclude the term, one has to prove understanding the topics, to be able to create new concepts and this has to be shown in the homeworks and problem solving and by the final discussion.
- Language of instruction
- Czech
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually. - Teacher's information
- https://is.muni.cz/auth/el/sci/podzim2023/M7111/index.qwarp
Detailed information, schedule of lectures and study materials for the current period are posted in the Interactive syllabus in IS.
- Enrolment Statistics (Autumn 2023, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2023/M7111