Course overview
This course develops a rigorous understanding of Bayesian statistical theory, with emphasis on coherent uncertainty quantification, Bayesian decision theory, statistical modelling, posterior and predictive inference, and model criticism. Students will learn to formulate Bayesian models, derive posterior and predictive analyses, interpret prior and likelihood assumptions, and use Bayesian evidence and approximation methods to support principled statistical reasoning.
- Foundations of Bayesian Reasoning
- Bayesian Models and Inference
- Advanced Inference and Remodelling
Course learning outcomes
- Propose, formulate, interpret, and defend Bayesian models for statistical inference problems.
- Derive posterior, predictive, and decision-theoretic results using advanced mathematical statistics.
- Critically evaluate prior assumptions, modelling choices, and inferential summaries in Bayesian analyses.
- Recommend and justify model refinements or model comparisons using Bayesian evidence.
Degree list
The following degrees include this course