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Basic notions of the probability and statistics will be introduced and examples of applications will be given. It concerns especially of the notion of probability, random variable and of its distribution, independence, random sample and its descriptive characteristics, construction of estimators, testing of hypotheses and random number generation. Emphasis will be especially on the practical use of above mentioned methods using freely available statistical software.
Last update: Kaplický Petr, doc. Mgr., Ph.D. (28.05.2019)
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The students will learn basics of the probability theory and mathematical statistics. The will be able to understand the core of stochastic procedures presented in other courses. Last update: Kaplický Petr, doc. Mgr., Ph.D. (28.05.2019)
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The credits for exercise classes are necessary condition for the exam. Conditions for the credits: 1. Attendance in the classes: at most 3 abseneces during the semester. 2. Written test: at least 51% of points. The nature of the credits excludes a retry. Condition 2. may be once retried. Last update: Zichová Jitka, RNDr., Dr. (01.05.2020)
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Karel Zvára, Josef Štěpán: Pravděpodobnost a matematická statistika, Matfyzpress, Praha, 2012. Ronald Meester. A Natural Introduction to Probability Theory 2nd ed. Birkhäuser 2008 Geoffrey Grimmett , David Stirzaker. Probability and Random Processes. Oxford 2001. Geoffrey Grimmett , David Stirzaker. One Thousand Exercises in Probability. Oxford 2001. Zápisky k přednášce dostupné na v MOodle UK https://dl1.cuni.cz/course/view.php?id=10744 Last update: Hlubinka Daniel, doc. RNDr., Ph.D. (29.09.2020)
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Lecture+exercises. Last update: Kaplický Petr, doc. Mgr., Ph.D. (28.05.2019)
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The written part of exam consists of numerical exercises. The oral part is focused on theory and its application. Last update: Zichová Jitka, RNDr., Dr. (01.05.2020)
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Introduction to probability theory and statistical induction. Axiomatic definition of probability, computation of probability, conditional probability and Bayes formula. Random variables and vectors and their distribution, characteristics of random variables. Convergence in probability and in distribution, law of large numbers and central limit theorem, Markov, Čebyšev and Chernoff inequalities. Applications of limit theorems and inequalities. Statistical estimation based on limit theorems. Last update: Zichová Jitka, RNDr., Dr. (01.05.2020)
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Knowledge required before enrollment: combinatorics, basic formulas elementary calculus (sequences, series, integrals), linear algebra. Last update: Zichová Jitka, RNDr., Dr. (01.05.2020)
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