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Identical coursesConcepts of Bayesian Data Analysis (B-KUL-G0B74A)
Cannot be taken as part of an examination contractAims
This course will give a broad introduction to basic concepts of Bayesian analysis. Posterior summary measures, predictive distributions and Bayesian hypothesis tests will be contrasted with the frequentist approach. Simulation methods such as Markov chain Monte Carlo (MCMC) enable the Bayesian analysis. An introduction to algorithms like Gibbs sampling and Metropolis-Hastings will be explained and illustrated. Various medical case studies will be considered.
The student should be able to analyse relatively simple problems in a Bayesian way using OpenBugs, Nimble or JAGS software. The emphasis in this course is on theoretical background of basic concepts and practical data analysis.
Previous knowledge
The student knows the basics of statistical inference, and statistical modelling.
Beginning conditions:
Basic concepts of statistical modelling
Linear models
Generalized linear models
Identical courses
This course is identical to the following courses:
G0B74B : Concepts of Bayesian Data Analysis
Is included in these courses of study
- Master of Statistics and Data Science (on campus) (Leuven) (European Master of Official Statistics (EMOS)) 120 ects.
- Master of Statistics and Data Science (on campus) (Leuven) (Statistics and Data Science for Biometrics) 120 ects.
- Master of Statistics and Data Science (on campus) (Leuven) (Statistics and Data Science for Industry) 120 ects.
- Master of Statistics and Data Science (on campus) (Leuven) (Statistics and Data Science for Social, Behavioral and Educational Sciences) 120 ects.
- Master of Statistics and Data Science (on campus) (Leuven) (Theoretical Statistics and Data Science) 120 ects.
- Courses for Exchange Students Faculty of Science (Leuven)
Activities
4 ects. Concepts of Bayesian Data Analysis (B-KUL-G0B74a)
Content
This course will give a broad introduction to basic concepts of Bayesian analysis. Posterior summary measures, predictive distributions and Bayesian hypothesis tests will be contrasted with the frequentist approach. Simulation methods such as Markov chain Monte Carlo (MCMC) enable the Bayesian analysis . An introduction to algorithms like Gibbs sampling and Metropolis-Hastings will be explained and illustrated. Various medical case studies will be considered.
The student should be able to analyse relatively simple problems in a Bayesian way using OpenBugs software. The emphasis in this course is on practical data analysis, but the basic concepts of the theoretical background will also be given.
Format: more information
The lectures will be given in a mixed video and live format and non-mandatory exercises and quizzes will be available to the students to practice in preparation of the final exam. One mandatory homework will be given during the semester and will count for the final score. This homework is a group project; group composition will be discussed during the lectures.
Is also included in other courses
Evaluation
Evaluation: Concepts of Bayesian Data Analysis (B-KUL-G2B74a)
Explanation
The final examination is a multiple choice, open book, exam. The written exam will include questions on (1) general understanding, interpretation of some result, and checking a theoretical result (similar as in exercises) and software-related questions. The homework will count for 30% of the final mark. The written exam will count for the remaining 70%. Second chance exam will be organized similar to first chance.