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Course, academic year 2019/2020
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Heavy-Tailed Distributions - NMTP570
Title in English: Rozdělení s těžkými chvosty
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:2/0 Ex [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: prof. Lev Klebanov, DrSc.
Class: M Mgr. PMSE
M Mgr. PMSE > Volitelné
Classification: Mathematics > Probability and Statistics
Annotation -
Last update: T_KPMS (16.05.2013)
The course is devoted to studying the theory of heavy tailed distributions and to dealing with stochastic models based on such distributions. Data with heavy tails have been collected in such fields as economics, telecommunications, physics and biology. The theory of heavy tailed distributions is also connected with the theory of branching processes.
Aim of the course -
Last update: T_KPMS (16.05.2013)

The aim of the lecture is to give an introduction to the theory of stochastic models with heavy tailed distributions.

Course completion requirements - Czech
Last update: RNDr. Jitka Zichová, Dr. (23.04.2018)

Složení ústní zkoušky.

Literature - Czech
Last update: T_KPMS (16.05.2013)

Klebanov, L.B.: Heavy tailed distributions. Matfyzpress, Praha, 2003.

Teaching methods -
Last update: T_KPMS (16.05.2013)

Lecture.

Requirements to the exam - Czech
Last update: RNDr. Jitka Zichová, Dr. (28.02.2018)

Zkouška sestává z ústní části. Známka ze zkoušky se stanoví na základě této části. Požadavky u ústní části odpovídají sylabu předmětu v rozsahu, který byl prezentován na přednášce.

Syllabus -
Last update: T_KPMS (16.05.2013)

1. Main examples of heavy tailed distributions.

2. Stable distributions.

3. Multivariate stable distributions.

4. Infinite divisible and stable distributions.

5. Il-posed problems.

Entry requirements -
Last update: RNDr. Jitka Zichová, Dr. (19.06.2019)

Measure theory based probability and mathematical statistics

 
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