Bayesian Data Analysis: Methods, Models, and Practical Applications
Course facts
P000165, Bayesian Data Analysis: Methods, Models, and Practical Applications, 2.5 Hp
Print syllabus
Syllabus
Finalized by: FUN-LTV, 2025-12-05
Valid from : Second half-year 2026 (2026-07-01)
Level
Third cycle
Subject
Agricultural Science
Grading Scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
• Basic knowledge of probability theory, particularly familiarity with common probability distributions (e.g., binomial, Poisson, normal, gamma, and beta distributions) and fundamental statistical inference, such as point and interval estimation and hypothesis testing. It is also advantageous if applicants have prior knowledge of simple and multiple linear regression.
• Ability to work in a statistical programming language (e.g., R, Python, or similar), including importing data, performing basic statistical analyses, and writing simple functions—preferably in R.
Objectives
After completing the course, the student will be able to:
1. Explain fundamental concepts of Bayesian inference and posterior updating.
2. Implement Bayesian regression models using R and Stan.
3. Evaluate and compare models using predictive and information criteria.
4. Quantify uncertainty and interpret credible intervals in the context of agricultural, forestry, environmental, and social science datasets.
5. Apply Bayesian analysis to real-world problems such as crop yield prediction, soil quality monitoring, climate resilience analysis, and modeling social attitudes or policy impacts.
6. Communicate Bayesian modeling results clearly and critically in a scientific context.
Content
Course Content:
• Introduction to Bayesian philosophy and inference
• Prior and posterior distributions; model comparison
• Hierarchical models and random effects
• MCMC methods and computational implementation (Stan)
• Model checking and posterior predictive validation
• Applications to real world problems such as crop modeling, soil moisture, environmental indicators and social attitudes or policy impacts
• Reproducible workflow and reporting Bayesian analyses
Course literature
Primary Texts:
• Donovan, T., and Ruth M. M. 2019. Bayesian Statistics for Beginners: A Step-by-Step Approach. Oxford: Oxford University Press.
• John K. Kruschke (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, 2nd Edition. Elsevier.
Supplementary Readings:
• Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., & Rubin, D. (2022). Bayesian Data Analysis, 3rd Edition. CRC Press.
• Hobbs, N.T. & Hooten, M.B. (2015). Bayesian Models: A Statistical Primer for Ecologists. Princeton University Press.
• Clark, J.S. (2007). Models for Ecological Data: An Introduction. Princeton University Press.
Examination Formats and Requirements for Passing the Course
• Active participation in lectures and exercises (attendance at a minimum of two-thirds of all sessions is required) • Successful completion and submission of all practical assignments. • A final mini-project that applies Bayesian modeling to a real dataset— preferably related to the participant’s own research area. This can be done individually or in groups. • All learning objectives must be demonstrated in the final assessment.
Responsible Department/Equivalent
Department of Biosystems and Technology
Supplementary information
Other Information
• Language of instruction: English
• Study venue: Alnarp / Hybrid (Zoom + on-site)
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