Notes
- Introduction
- Priors
- Bayesian theory
- MCMC algorithms and implementation
- Basic models: GLMMs and hierarchical models
- Model selection and evaluation: Bayes factors, selection criteria, and model checks
- Advanced modeling: high-dimensional models, nonparametric regression, Bayesian nonparametrics
- Advanced computing: scalability, intractable likelihoods