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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 problems Last update: T_KPMS (15.05.2013)
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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.
Last update: Komárek Arnošt, prof. RNDr., Ph.D. (12.10.2017)
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Hušková M.: Bayesovské metody, UK Praha, skripta, 1985
Pázman, A.: Bayesovská štatistika, UK Bratislava, skripta, 2003.
Robert, C.P.: The Bayesian choice, Springer, 2001.
Last update: T_KPMS (15.05.2013)
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Lecture+exercises. Last update: T_KPMS (15.05.2013)
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The subject is finalized by a tutorial credit and an exam. Only the students who have obtained the tutorial credit can attempt to take the exam. The exam is combined written and oral.
Tutorial credit requirements:
1. Regular small assignments: A student needs to prepare and deliver on time acceptable solutions to all assignments.
2. Project: A student needs to submit a project satisfying the requirements given in the assignment. A corrected version of an unsatisfactory project can be resubmitted once.
The nature of these requirements precludes any possibility of additional attempts to obtain the tutorial credit. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (17.02.2023)
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Bayes theorem and its use, prior and posterior distribution, methods to choose a prior distribution.
Statistical decision functions.
Bayes point estimators and their properties. Credible sets.
Bayes hypothesis testing, some special tests.
Some special bayesian approches, basics of MCMC. Last update: Komárek Arnošt, prof. RNDr., Ph.D. (29.10.2019)
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Last update: Komárek Arnošt, prof. RNDr., Ph.D. (25.05.2018)
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