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Lecturer(s)
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Fürst Tomáš, RNDr. Ph.D.
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Course content
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1. Two approaches to probability: Kolmogorov and Cox. 2. Conditional probability, likelihood. 3. Inference, prediction, and decision. 4. Bayes' Theorem and its application. 5. Exact methods of inference. 6. Maximum likelihood method. 7. Laplace's method of approximate inference. 8. Model comparison. 9. Monte Carlo methods.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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Understand the principles of Bayesian approach to data and inference
Comprehension: Understand the principles of Bayesian approach to data and inference
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Prerequisites
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understanding linear algebra and calculus, basic procedural programming
KMA/MA1 and KAG/LA1A
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Assessment methods and criteria
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Seminar Work
Colloquium: present a solution to a problem of inference/prediction/decision
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Recommended literature
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Online přednáška.
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Online přednáška.
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A. B. Downey. (2013). Think Bayes. O'Reilly.
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D. MacKay. (2003). Information theory, Inference, and learning algorithms. Cambridge University Press.
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Gelman. (2013). Bayesian data analysis, Series: Chapman & Hall/CRC Texts in Statistical Science. Chapman and Hall.
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J. Kruschke. (2014). Doing Bayesian Data Analysis: A Tutorial with R. JAGS, and Stan, Academic Press.
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R. McElreath. (2015). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall.
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T. Hastie R. Tibshirani. (2016). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer.
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