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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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Oral exam
Oral exam. Course credit: comprehensive analysis of a selected problem.
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Recommended literature
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Downey, A. B. (2013). Think Bayes. O'Reilly.
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Gelman, A. (2013). Bayesian data analysis, Series: Chapman & Hall/CRC Texts in Statistical Science. Chapman and Hall.
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Kruschke, J. (2014). Doing Bayesian Data Analysis: A Tutorial with R. JAGS and Stan.
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MacKay, D. (2003). Information theory, Inference, and learning algorithms.
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McElreath, R. (2015). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall.
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Vencálek, O. (2021). Úloha o rozdělení sázky ? tradiční a bayesovský pohled. Informační bulletin ČStS 4/2021.
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