Course details

# Statistics and Probability

MSP Acad. year 2022/2023 Winter semester 5 credits

Summary of elementary concepts from probability theory and mathematical statistics. Limit theorems and their applications. Parameter estimate methods and their properties. Scattering analysis including post hoc analysis. Distribution tests, tests of good compliance, regression analysis, regression model diagnostics, non-parametric methods, categorical data analysis. Markov decision-making processes and their analysis, randomized algorithms.

Guarantor

Course coordinator

Language of instruction

Completion

Time span

- 26 hrs lectures
- 21 hrs exercises
- 5 hrs projects

Assessment points

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

Department

Lecturer

Češka Milan, doc. RNDr., Ph.D. (UITS)

Hrabec Pavel, Ing., Ph.D. (IM DSO)

Žák Libor, doc. RNDr., Ph.D. (IM DSO)

Instructor

Benko Matej, Ing. (IM)

Češka Milan, doc. RNDr., Ph.D. (UITS)

Hrabec Pavel, Ing., Ph.D. (IM DSO)

Žák Libor, doc. RNDr., Ph.D. (IM DSO)

Subject specific learning outcomes and competences

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

Learning objectives

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.

Why is the course taught

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.

Prerequisite knowledge and skills

Foundations of differential and integral calculus.

Foundations of descriptive statistics, probability theory and mathematical statistics.

Study literature

- 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 K.. Regresní analýza, Academia, Praha, 1989
- Meloun M., Militký J.: Statistické zpracování experimentálních dat (nakladatelství PLUS, 1994).
- D. P. Bertsekas, J. N. Tsitsiklis. Introduction to Probability, Athena, 2008. Scientific

Syllabus of lectures

- Markov processes and their analysis.
- Markov decision processes and their basic analysis.
- Introduction to randomized algorithms and their use (Monte Carlo, Las Vegas, applications).
- 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.
- Extension of hypothesis tests for binomial and normal distributions.
- Analysis of variance (simple sorting, ANOVA), post hos analysis.
- Regression analysis. Creating a regression model. Testing hypotheses about regression model parameters. Comparison of regression models. Diagnostics.
- Distribution tests.
- Estimation of parameters using the method of moments and the maximum likelihood method.
- Bayesian approach and construction of Bayesian estimates.
- Nonparametric methods of testing statistical hypotheses - part 1.
- Nonparametric methods of testing statistical hypotheses - part 2
- Analysis of categorical data. Contingency table. Independence test. Four-field tables. Fisher's exact test.

Syllabus of numerical exercises

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

Syllabus - others, projects and individual work of students

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

Progress assessment

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.**

Controlled instruction

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.

Exam prerequisites

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

Schedule

Day | Type | Weeks | Room | Start | End | Capacity | Lect.grp | Groups | Info |
---|---|---|---|---|---|---|---|---|---|

Tue | lecture | 1., 2., 3. of lectures | D105 | 10:00 | 11:50 | 9999 | 1MIT 2MIT | NBIO - NSPE xx | Češka |

Tue | lecture | 4., 5., 6., 7., 8., 9., 10., 11., 12., 13. of lectures | D105 | 10:00 | 11:50 | 9999 | 1MIT 2MIT | NBIO - NSPE xx | Žák |

Tue | exercise | 1., 2., 3. of lectures | A113 | 12:00 | 13:50 | 35 | 1MIT 2MIT | xx | Češka |

Tue | exercise | 4., 5., 6., 7., 8., 9., 10., 11., 12., 13. of lectures | A113 | 12:00 | 13:50 | 35 | 1MIT 2MIT | xx | Žák |

Tue | exam | 2023-01-24 | D105 | 13:00 | 15:00 | 1. oprava | |||

Tue | exam | 2023-01-10 | D0206 D0207 D105 | 14:00 | 16:50 | řádná | |||

Wed | exercise | 1., 3. of lectures | D0207 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Češka |

Wed | exercise | 4., 5., 6., 7., 8., 9., 10., 11., 12., 13. of lectures | D0207 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Hrabec |

Wed | exercise | 1., 3. of lectures | D0207 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Češka |

Wed | exercise | 4., 5., 6., 7., 8., 9., 10., 11., 12., 13. of lectures | D0207 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Hrabec |

Thu | exercise | 1., 2., 3. of lectures | G202 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Andriushchenko |

Thu | exercise | 4., 5., 6., 7., 8., 10., 11., 12., 13. of lectures | G202 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Benko |

Thu | exercise | 1., 2., 3. of lectures | G202 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Andriushchenko |

Thu | exercise | 4., 5., 6., 7., 8., 10., 11., 12., 13. of lectures | G202 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Benko |

Fri | exercise | 1., 2., 3. of lectures | G202 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Andriushchenko |

Fri | exercise | 4., 5., 7., 8., 9., 10., 11., 12., 13. of lectures | G202 | 08:00 | 09:50 | 35 | 1MIT 2MIT | xx | Hrabec |

Fri | exercise | 1., 2., 3. of lectures | G202 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Andriushchenko |

Fri | exercise | 4., 5., 7., 8., 9., 10., 11., 12., 13. of lectures | G202 | 10:00 | 11:50 | 35 | 1MIT 2MIT | xx | Hrabec |

Fri | exam | 2023-02-03 | D105 | 13:00 | 15:00 | 2. oprava | |||

Fri | exam | 2022-10-21 | D0206 D105 | 17:00 | 19:00 | 1. test |

Course inclusion in study plans