M8BDA Bayesian Data Analysis with R and BUGS

Faculty of Science
Spring 2024
Extent and Intensity
11/8/0. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
prof. Pablo Emilio Verde (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (assistant)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Course outline
The course is intended as a first course in Bayesian data analysis. The course covers from basic Bayesian statistics to an introduction to hierarchical models. The course presentation is practical with many worked examples using the free statistical software R and WinBUGS.
Who should attend?
Students and data analyst with knowledge in statistical methods will benefit from this course. Participants should be familiar with some statistical concepts, preferably up to regression analysis.
Learning outcomes
After completing the course, students can
• Understand the role of Bayesian Analysis in a broad spectrum of data analysis.
• Use R, MultiBUGS and JAGS software for Bayesian statistical modelling.
• Students will be able to apply simple Bayesian hierarchical models in data analysis.
• At the end of the course students will be able to implement and develop Bayesian models in new situations of data analysis. Participants' prerequisites: To attend the course, participants need to have a good background in classical statistics and a working knowledge of the statistical software R.
Syllabus
  • During the last decades Bayesian inference has become a popular topic in applied and theoretical statistics. One of the main reasons for this success has been the development of free and open-source statistical software to perform MCMC (Markov Chain Monte Carlo) computations. These computational techniques allow Bayesian statistical models that reflect the complexity of complexity of the data (e.g. hierarchical structures, missing data, outliers). The aim of this course is to give a practical introduction to Bayesian data analysis using the statistical software R and the computer simulation software BUGS and JAGS. The following topics will be covered during the course:
  • Day 1: half a day (Monday ?)
  • Morning or Afternoon
  • 3 lectures and 2 practicals
  • • Introduction to Bayesian inference
  • • Bayesian statistical of simple statistical models
  • • Using R, MultiBUGS and JAGS for simple Bayesian models
  • Day 2: full day (Tuesday)
  • Morning: 2 lectures and 2 practicals
  • Afternoon: 2 lectures and 2 practicals
  • • The role of prior distributions in Bayesian inference
  • • Bayesian analysis of multiple parameters models
  • • Bayesian analysis of regression models
  • • Introduction to Bayesian computations (Gibb sampling, Metropolis sampling, etc.)
  • Day 3: full day (Wednesday)
  • Morning: 2 lectures and 1 practical
  • Afternoon: 2 lectures and 1 practical
  • • Introduction to Hierarchical Modeling
  • • Longitudinal data analysis
  • • Bayesian analysis of special models: models for missing data, non-parametric models
Literature
    recommended literature
  • 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
  • LUNN, David. The BUGS book : a practical introduction to Bayesian analysis. Boca Raton: CRC Press, 2013, xvii, 381. ISBN 9781584888499. info
  • SPIEGELHALTER, D. J., K. R. ABRAMS and Jonathan P. MYLES. Bayesian approaches to clinical trials and health-care evaluation. Hoboken: John Wiley & Sons, 2004, xiv, 391. ISBN 0471499757. info
    not specified
  • Software:

    R: https://cran.r-project.org/

    MultiBUGS: https://www.multibugs.org/

    JAGS: https://mcmc-jags.sourceforge.io/

Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught only once.
General note: Výuka proběhne v týdnu od 13.5. do 17. 5. 2024.
The course is also listed under the following terms Spring 2013.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2024/M8BDA