M9211 Bayesian methods

Faculty of Science
Spring 2022
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Taught in person.
Mgr. Ondřej Pokora, Ph.D. (lecturer)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics - Departments - Faculty of Science
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
Students are supposed to have theoretical knowledge as well as practical experience in the topics of basic courses of Probability and mathematical statistics and Calculus. The experience in working with software environment R is supposed, too.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
This course introduces the basic principles and methods of the Bayesian statistics. The student learns the theoretical principles of these methods and the techniques for calculation of the estimators and their application in inference and prediction. The course also deals with the basics of information theory and with the numerical and simulation methods of the calculations. In the practical classes, the student learns how to calculate the aposterior density and the Bayesian estimators in real problems, how to interpret them and how to compare them with the classical statistical estimators. Further, the student goes through the computer implementation of the methods of numerical integration and Markov-Chain-Monte-Carlo simulations.
Learning outcomes
After completing this course, the student will be able to:
- understand the methods of the Bayesian statistics and to interpret their parameters;
- calculate the Bayesian estimators and to infer in real problems;
- compare the Bayesian and the frequentist approach;
- evaluate the aposterior density by approximations and by the Markov-Chain-Monte-Carlo methods using computers;
- evaluate the I-divergence and the information gained from the experiment.
  • Bayes' theorem and aposterior probability density.
  • Bayes' formula for discrete and continuous random variables.
  • Chain rule.
  • I-divergence and information gained from the experiment.
  • Entropy and mutual information.
  • Noninformative priors.
  • Computational methods for evaluation of the aposterior probability density.
  • Conjugate systems of the prior probability densities.
  • Simulation methods, Monte-Carlo integration.
  • Markov-Chain-Monte-Carlo methods.
  • Calculation of the Bayesian (point and interval) estimation, inference and prediction.
  • ALBERT, Jim. Bayesian computation with R. 2nd ed. Dordrecht: Springer, 2009. xii, 298. ISBN 9780387922973. info
  • CHRISTENSEN, Ronald. Bayesian ideas and data analysis an introduction for scientists and statisticians. Boca Raton: CRC Press, 2011. xvii, 498. ISBN 9781439803547. info
  • GELMAN, Andrew, John B. CARLIN, Hal Steven STERN, David B. DUNSON, Aki VEHTARI and Donald B. RUBIN. Bayesian data analysis. Third edition. Boca Raton: CRC Press/Taylor & Francis, 2014. xiv, 667. ISBN 9781439840955. info
  • HOFF, Peter D. A first course in Bayesian statistical methods. Dordrecht: Springer, 2009. ix, 270. ISBN 9780387922997. info
  • HUŠKOVÁ, Marie. Bayesovské metody (Bayesian methods). Praha: Univerzita Karlova v Praze, 1985. 93 pp. info
  • PÁZMAN, Andrej. Bayesovská štatistika (Bayesian statistics). Bratislava: Univerzita Komenského Bratislava, 2003. 100 pp. ISBN 80-223-1821-3. info
  • ROBERT, Christian P. The Bayesian choice : from decision-theoretic foundations to computational implementation. 2nd ed. New York: Springer, 2007. xxiv, 602. ISBN 9780387715988. info
Teaching methods
Lectures: 2 hours a week. Practical classes: 2 hour a week, work with mathematical software R. Distance form: online lectures, practical classes and discussions.
Assessment methods
Exercises: active participation in online course and discussions, solving homeworks and project. Depending on the current epidemiological situation, the form of the final will be decided. Full-time form of the final exam: (1) practical (PC), (2) written and (3) oral part. Distance form of the final exam: (1) practical (PC) and online electronic part and (3) oral part (one-to-one online talk). For successful completion, it is necessary to achieve at least 50 % of the maximum total points.
Language of instruction
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms spring 2018, Spring 2019, Spring 2020, Spring 2021.
  • Enrolment Statistics (recent)
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