CUrriculum

Monday - Classes from 9:30 to 17:30

 

Foundations of Bayesian inference

 

- Probability theory and Bayes-Price-Laplace's rule 
- Probability distributions 
- Understanding and eliciting priors
- Analytical Bayes: Beta-Binomial, Poisson-Gamma, Normal-Normal

 

 

Tuesday - Classes from 9:30 to 17:30

 

Computational Bayes

 

- Generating prior predictive distributions using RStan and R
- Fake-data simulation for model evaluation
- Sampling methods:
- Inverse sampling
- Gibbs sampling
- Random Walk Metropolis
- Hamiltonian Monte Carlo

 

 

Wednesday - Classes from 9:30 to 17:30

 

 Bayesian Modeling with Stan and brms

 

- Introduction to Stan syntax
- Introduction to brms
- Linear models using RStan and brms

 

Thursday - Classes from 9:30 to 17:30

 

Regression modeling using Stan and brms

 

- Generalized linear models
- Model evaluation and calibration 
- Model comparison using LOO and Bayes factor

 

Friday - Classes from 9:30 to 17:30


Model evaluation and comparison

 

- Hierarchical linear models 
- Fake-data generation for hierarchical data 
- Posterior predictive checks
- Some instructive case studies