Statistics and Probability

• 26 hrs lectures
• 21 hrs exercises
• 5 hrs projects

• 70 pts final exam (written part)
• 20 pts written tests (test part)
• 10 pts projects

Students will extend their knowledge of probability and statistics, especially in the following areas:

• Parameter estimates for a specific distribution
• simultaneous testing of multiple parameters
• hypothesis testing on distributions
• regression analysis including regression modeling
• nonparametric methods
• creation of parameter estimates
• Bayesian statistics
• Markov processes
• randomised algorithms 

Introduction of further concepts, methods and algorithms of probability theory, descriptive and mathematical statistics. Development of probability and statistical topics from previous courses. Formation of a stochastic way of thinking leading to formulation of mathematical models with emphasis on information fields.

The society development desires also technology and, in particular, information technology expansion. It is necessary to process information - data in order to control technology. Nowadays, there is a lot of devices that collect data automatically. So we have a large amount of data that needs to be processed. Statistical methods are one of the most important means of processing and sorting data, including their analysis. This allows us to obtain necessary information from your data to evaluate and control.

Foundations of differential and integral calculus.

Foundations of descriptive statistics, probability theory and mathematical statistics.

• Anděl, Jiří. Základy matematické statistiky. 3.,  Praha: Matfyzpress, 2011. ISBN 978-80-7378-001-2.
• FELLER, W.: An Introduction to Probability Theory and its Applications. J. Wiley, New York 1957. ISBN 99-00-00147-X
• Hogg, V.R., McKean J.W. and Craig A.T. Introduction to Mathematical Statistics. Seventh Edition, 2012. Macmillan Publishing Co., INC. New York. ISBN-13: 978-0321795434  2013
• Zvára, Karel. Regrese. 1., Praha: Matfyzpress, 2008. ISBN 978-80-7378-041-8
• Meloun M., Militký J.: Statistické zpracování experimentálních dat (nakladatelství PLUS, 1994).
• D. P. Bertsekas, J. N. Tsitsiklis. Introduction to Probability, Athena, 2008. Scientific

1. Markov processes and their analysis.
2. Markov decision processes and their basic analysis.
3. Introduction to randomized algorithms and their use (Monte Carlo, Las Vegas, applications).
4. Summary and recall of knowledge and methods used in the subject of IPT. An outline of other areas of probability and statistics that will be covered.
5. Extension of hypothesis tests for binomial and normal distributions.
6. Analysis of variance (simple sorting, ANOVA), post hos analysis.
7. Regression analysis. Creating a regression model. Testing hypotheses about regression model parameters. Comparison of regression models. Diagnostics.
8. Distribution tests.
9. Estimation of parameters using the method of moments and the maximum likelihood method.
10. Bayesian approach and construction of Bayesian estimates.
11. Nonparametric methods of testing statistical hypotheses - part 1.
12. Nonparametric methods of testing statistical hypotheses - part 2
13. Analysis of categorical data. Contingency table. Independence test. Four-field tables. Fisher's exact test.

1. Application and analysis of Markov processes.
2. Basic application and analysis of Markov decision processes.
3. Design and analysis of basic randomised algorithms.
4. Reminder of discussed examples in the IPT subjekt
5. Hypothesis tests for binomial and normal distributions.
6. Project assignment, analysis of variance, post host analysis.
7. Regression analysis.
8. Tests on distribution, tests of good agreement.
9. The method of moments and the maximum likelihood method.
10. Bayesian estimates.
11. Nonparametric methods of testing statistical hypotheses - part 1.
12. Nonparametric methods of testing statistical hypotheses - part 2.
13. Analysis of categorical data. Contingency table. Four-field tables

1.  Usage of tools for solving statistical problems (data processing and interpretation).

Two tests will be written during the semester - 5th and 10th week. The exact term will be specified by the lecturer. The test duration is 90 minutes. The evaluation of each test is 0-10 points.

Projected evaluated: 0-10 points.

Final written exam: 0-70 points. Students have to achieve at least 30 points, otherwise the exam is assessed by 0 points.

Participation in lectures in this subject is not controlled

Participation in the exercises is compulsory. During the semester two abstentions are tolerated. Replacement of missed lessons is determined by the leading exercises.

Fulfil the attendance conditions and achieve in total at least 15 points from the tests and the project.