Code: 01PRST Probability and Statistics
Lecturer: doc. Ing. Tomáš Hobza Ph.D. Weekly load: 3+1 Completion: A, EX
Department: 14101 Credits: 4 Semester: W
Description:
It is a basic course of probability theory and mathematical statistics. The probability theory is build gradually beginning with the classical definition and continuing till the Kolmogorov definition. The notions as random variable, distribution function of random variable and characteristics of random variable are treated and basic limit theorems are stated and proved. On the basis of this theory the basic methods of mathematical statistics such as estimation of distribution parameters and hypothesis testing are explained.
Contents:
1. Classical definition of probability, statistical definition of probability, conditional probability and Bayes's theorem
2. Random variables, distribution functions, discrete and continuous random variables, independent random variables, characteristics of random variable
3. Law of large numbers, central limit theorem
4. Point estimation, confidence intervals
5. Tests of statistical hypotheses, goodness of fit tests
Seminar contents:
1. Combinatorial rules, classical and geometric probability
2. Conditioned probability and related theorems
3. Distribution function of random variable, discrete and continuous random variables, transformation of random variables
4. Characteristics of random variables, mainly expectation and variance, central limit theorem
5. Point estimation of parameters
6. Hypothesis testing, goodness-of-fit tests
Recommended literature:
Key references:
[1] H. G. Tucker: An introduction to probability and mathematical statistics. Academic Press, 1963
[2] H. Pishro-Nik: Introduction to Probability, Statistics, and Random Processes, Kappa Research, LLC, 2014

Recommended references:
[3] J. Shao: Mathematical statistics, Springer, 2003
Keywords:
Random variable, distribution function, probability mass function, probability density, independence of random variables, expectation, variance, central limit theorem, point estimation of parameters, hypothesis testing, goodness-of-fit tests.

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