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Lecturer(s)
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Fišerová Eva, doc. RNDr. Ph.D.
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Hron Karel, prof. RNDr. Ph.D., DSc.
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Course content
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1. Probability and random variable 2. Random vector 3. Distributions of random variables and vectors 4. Convergence of random variables 5. Measure and probability
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Learning activities and teaching methods
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Work with Text (with Book, Textbook)
- Preparation for the Exam
- 120 hours per semester
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Learning outcomes
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To learn basics of probability theory.
Comprehension Understanding of basics of probability theory.
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Prerequisites
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Mathematical analysis and linear algebra on master level in Applied Mathematics.
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Assessment methods and criteria
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Oral exam
Oral exam: to know and to understand the subject.
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Recommended literature
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Anděl, J. (2011). Základy matematické statistiky. Praha.
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Billingsley, P. (2012). Probability and Measure. Wiley.
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Capiński, M., Kopp, E. (2004). Measure, integral and probability. London.
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Hogg, R. V., McKean, J. W., Craig, A.T. (2018). Introduction to mathematical statistics. Prentice Hall.
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Hron, K., Kunderová, P., Vencálek, O. (2018). Základy počtu pravděpodobnosti a metod matematické statistiky. Olomouc.
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