Statistics and Probability
| Code | Completion | Credits | Range |
|---|---|---|---|
| B6B01PRA | Z,ZK | 5 | 2P+2S+1D |
- Garant předmětu:
- Kateřina Helisová
- Lecturer:
- Kateřina Helisová
- Tutor:
- Kateřina Helisová, Miroslav Korbelář, Jakub Staněk
- Supervisor:
- Department of Mathematics
- Synopsis:
-
The students will be introduced to the theory of probability and mathematical statistics, namely to the basic computing methods and their applications in practice. The course covers the basic parts of probability and mathematical statistics. The first part is focused on classical probability, including conditional probability. The next part deals with the theory of random variables and their distributions, examples of the most important types of discrete and continuous distributions, numerical characteristics of random variables, their independence, sums and transformations. Probabilistic knowledge is then used in the description of statistical methods for estimating distribution parameters and testing hypotheses.
- Requirements:
-
Calculation of basic derivatives and integrals. Basics of combinatorics.
- Syllabus of lectures:
-
1. Random events, probability, probability space.
2. Conditional probability, Bayes' theorem, independent events.
3. Random variable - definition, distribution function, probability function, density.
4. Characteristics of random variables - expected value, variance and other moments.
5. Discrete random variable - examples and usage.
6. Continuous random variable - examples and usage.
7. Independence of random variables, covariance, correlation.
8. Transformation of random variables, sum of independent random variables (convolution).
9. Random vector, covariance and correlation.
10. Central limit theorem.
11. Random sampling and basic statistics.
12. Point estimates, maximum likelihood method and method of moments.
13. Confidence intervals.
14. Hypotheses testing.
- Syllabus of tutorials:
-
1. Combinatorics, random events, probability, probability space.
2. Conditional probability, Bayes' theorem, independent events.
3. Random variable - construction and usage of distribution function, probability function and density.
4. Characteristics of random variables - expected value, variance.
5. Discrete random variable - examples and usage.
6. Continuous random variable - examples and usage.
7. Independence of random variables, covariance, correlation.
8. Transformation of random variables, sum of independent random variables (convolution).
9. Random vector, joint and marginal distribution.
10. Central limit theorem.
11. Random sampling and basic statistics, point estimates, maximum likelihood method and method of moments.
12. Confidence intervals.
13. Hypotheses testing.
14. Reserve.
- Study Objective:
-
The students will be introduced to the theory of probability and mathematical statistics, namely to the basic computing methods and their applications in practice.
- Study materials:
-
[1] Papoulis, A.: Probability and Statistics, Prentice-Hall, 1990.
[2] Stewart W.J.: Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press 2009.
- Note:
- Further information:
- https://math.fel.cvut.cz/en/people/heliskat/01pst.html
- Time-table for winter semester 2023/2024:
- Time-table is not available yet
- Time-table for summer semester 2023/2024:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - The course is a part of the following study plans:
-
- Software Engineering and Technology (compulsory course in the program)
- Software Engineering and Technology (compulsory course in the program)
- Software Engineering and Technology (compulsory course in the program)
- Software Engineering and Technology (compulsory course in the program)
- Software Engineering and Technology (compulsory course in the program)