Course: Probability Theory

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Course title Probability Theory
Course code KMA/TP
Organizational form of instruction Lecture + Tutorial
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course unspecified
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Friesl Michal, Mgr. Ph.D.
Course content
FOR SUMMER SEMESTER OF SCHOOL YEAR 2023/2024 Probability measure. Outcomes of the random experiment, algebra and sigma-algebra of events. Finitely additive and sigma-additive probability, probability measure, probability space, examples. Random variables. Random variable with general state space and its distribution. Discrete and continuous distribution, density. Stochastic process. Stochastic process, product sigma-algebra, existence of the distribution of the process. Random vectors and sequences, process with values in R and continuous trajectories. Real random variable. Real random variable and vector. Distribution function, discrete, continuous and singular component. Mean and other moments. Characteristic function, relationship with moments. Convergence. Convergences of random variables: point, almost certainly, in probability, in the mean. Weak convergence of probability measures, convergence in distribution, convergence of distribution and chrakteristic functions. Mutual relationships, convergence of transformed variables. Independence. Independence of systems of events and random variables, product measure. Zero-one laws. Borel and Cantelli lemma. Tail and symmetric events, Kolmogorov and Hewitt-Savage zero-one law. The law of large numbers. Chebyshev's weak law of large numbers, strong law of large numbers for independent and identically distributed variables. Central limit theorem. Lindeberg-Levy central limit theorem, Feller-Lindeberg and Lyapunov condition. Conditional expected value. Definition of the conditional expected value, conditioning with respect to sigma-algebras and random variables, conditional density, conditional probability. Properties of the conditional expected value as an integral, taking out, independence, conditional expected valule as a projection. System of conditional distributions. Additional information on the web page http://home.zcu.cz/~friesl/Vyuka/Tp.html

Learning activities and teaching methods
Lecture with practical applications, Students' self-study, Self-study of literature, Textual studies, Lecture, Practicum
  • Contact hours - 52 hours per semester
  • Preparation for formative assessments (2-20) - 39 hours per semester
  • Preparation for comprehensive test (10-40) - 20 hours per semester
  • Preparation for an examination (30-60) - 50 hours per semester
prerequisite
Knowledge
formulovat a vysvětlit základní pojmy teorie míry a Lebesgueova integrálu (v rozsahu předmětu KMA/MA5)
formulovat a vysvětlit základní pojmy pravděpodobnosti a statistiky (v rozsahu předmětu KMA/PSA)
Skills
pracovat s abstraktními strukturami teorie míry
vypočítat určité i neurčité integrály (známých typů)
využívat znalostí základních statistických metod a postupů pro jednoduchou analýzu dat
odlišit různé typy náhodných veličin (diskrétní, spojité) a různé typy rozdělení
Competences
N/A
N/A
N/A
learning outcomes
Knowledge
orientovat se v probraných pojmech a výsledcích teorie pravděpodobnosti
Skills
formulovat přesně matematicky probrané pojmy a výsledky teorie pravděpodobnosti
odvodit vyložené vlastnosti a vztahy
Competences
N/A
N/A
teaching methods
Knowledge
Lecture
Interactive lecture
Self-study of literature
Practicum
Textual studies
Skills
Interactive lecture
Self-study of literature
Lecture
Textual studies
Practicum
Competences
Textual studies
Lecture
Practicum
Interactive lecture
Self-study of literature
assessment methods
Knowledge
Oral exam
Skills demonstration during practicum
Written exam
Skills
Skills demonstration during practicum
Written exam
Oral exam
Competences
Skills demonstration during practicum
Written exam
Oral exam
Recommended literature
  • Kallenberg, Olav. Foundations of modern probability. 2nd ed. New York : Springer, 2002. ISBN 0-387-95313-2.
  • Lachout, Petr. Teorie pravděpodobnosti. Praha : Karolinum, 2004. ISBN 80-246-0872-3.
  • Štěpán, Josef. Teorie pravděpodobnosti : Matematické základy : Vysokošk. učebnice pro stud. matematicko-fyz. fakult. Praha : Academia, 1987.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester