Course overview
Bayesian statistics is a powerful framework for statistical analysis. This course will introduce students to this framework by building on previous knowledge of generalised linear modelling with a Bayesian perspective. Learners will gain skills in Bayesian workflow and more advanced modelling tools like mixed effects models, with computational details reinforced in subsequent courses.
Course learning outcomes
- Critically compare frequentist and Bayesian approaches for the same model type
- Use Bayesian workflow to interrogate and validate model output
- Explain the benefits and weakness of partial pooling
- Experiment with different priors and critically evaluate results
- Identify an appropriate model comparison technique and interpret the results for an application