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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. The Bayesian viewpoint: conditional probability, likelihood, inference, prediction, decision making 2. Monte Carlo Methods, efficient MCMC, Ising models 3. Variational methods 4. Case study: Bayesian methods in justice and expert opinions 5. Decision theory 6. Gaussian processes 7. Probabilistic graphical models 8. Causal Inference
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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Understand the principles of bayesian inference, prediction and decision making Be able to perform the computations by means of standard software
Understanding the principles of bayesian inference, prediction and decision making computation by means of standard software
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Prerequisites
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single variable calculus linear algebra simple programming skills
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Assessment methods and criteria
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Dialog, Seminar Work
Credits: active participation, presentation of a solved problem Exam: scientific conversation in a group
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Recommended literature
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A. B. Downey. (2013). Think Bayes. O'Reilly.
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A. Gelman. (2013). Bayesian data analysis, Series: Chapman & Hall/CRC Texts in Statistical Science. Chapman and Hall.
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C. E. Rasmussen, C. Williams. (2006). Gaussian Processes for Machine Learning. MIT Press.
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D. Barber. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
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D. MacKay. (2003). Information theory, Inference, and learning algorithms. Cambridge University Press.
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J. Kruschke. (2014). Doing Bayesian Data Analysis: A Tutorial with R. JAGS, and Stan, Academic Press.
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Martin, Osvaldo A.; Kumar, Ravin; Lao, Junpeng. (2021). Bayesian Modeling and Computation in Python.
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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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