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Prior and posterior distributions, conjugate families, Bayesian test and estimators, applications.
Last update: T_KPMS (15.05.2013)
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Basic principles of Bayesian approach to statistical modelling Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
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The course credit for the exercise class will be awarded to the student who hands in a satisfactory solution to each homework assignment by the prescribed deadline. Solutions found not satisfactory enough will be sent back for revision; each student may revise up to two solutions.
Last update: Vávra Jan, RNDr., Ph.D. (31.08.2026)
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Hušková, M. (1985). Bayesovské metody (course notes in Czech). Praha: Univerzita Karlova v Praze. Robert, C. P. (2001, 2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, Second Edition. New York: Springer. Marin, J.-M., Robert, C. P. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. New York: Springer. Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B., Dunson, D. B. (2014). Bayesian Data Analysis, Third Edition. Boca Raton: Chapman & Hall/CRC. Carlin, B. P., Louis, T. A. (2008). Bayesian Methods for Data Analysis, Third Edition. Boca Raton: Chapman & Hall/CRC. Robert, C. P., Casella, G. (2004). Monte Carlo Statistical Methods, Second Edition. New York: Springer. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
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Lecture+exercises. Last update: T_KPMS (15.05.2013)
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The course concludes with a credit assignment and an exam. To take the exam, students must first earn the course credit.
The exam consists of the following parts: 1. A discussion of the exam project solution. 2. An oral part (with written preparation) covering the material presented in lectures and seminars.
The exam grade combines parts 1 and 2. If either part 1 or 2 is graded as unsatisfactory, the exam grade will be “failed.” During the retake, only the part that was graded as unsatisfactory during the previous exam will be retaken. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
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Bayes’ theorem and its applications, prior and posterior distributions, methods for choosing a prior distribution.
Bayesian point estimates, credibility sets.
Monte Carlo and Markov chain Monte Carlo (MCMC) Bayesian inference.
Hierarchical models and the Bayesian approach to working with them.
Bayesian data augmentation. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (31.08.2026)
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Last update: Komárek Arnošt, prof. RNDr., Ph.D. (25.05.2018)
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