| Course title | Mathematical Statistics |
|---|---|
| Course code | KMA/PGSA3 |
| Organizational form of instruction | Lecture |
| Level of course | Doctoral |
| Year of study | not specified |
| Semester | Winter and summer |
| Number of ECTS credits | 5 |
| Language of instruction | Czech, English |
| Status of course | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
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| Course content |
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1. Elementary statistical inference 2. Maximum likelihood method 3. Sufficient statistics 4. Hypotheses testing 5. Inference under the normality assumption 6. Nonnaprametric statistics 7. Bayesian statistics 8. Linear models
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| Learning activities and teaching methods |
Work with Text (with Book, Textbook)
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| Learning outcomes |
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To get an overview about methods of mathematical statistics.
Comprehension Understanding of basic methods of mathematical statistics. |
| Prerequisites |
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Basic course in probability theory (doctoral level).
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| Assessment methods and criteria |
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Oral exam
Oral exam: to know and to understand the subject. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics (2020) | Category: Mathematics courses | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics (2025) | Category: Mathematics courses | - | Recommended year of study:-, Recommended semester: - |