Monday - Classes from 9:30 to 17:00

 

 

Foundations of Bayesian inference (Chapters 1 and 2 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

- Review of probability theory and Bayes-Price-Laplace's rule

 

- Probability distributions

 

- Analytical Bayes: Beta-Binomial

 

 

 

 

Tuesday -Classes from 9:30 to 17:00

 

 

 

 Computational Bayes with brms (Chapter 3 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

 

- Introduction to brms with a normal likelihood

 

- Selection of priors

 

- Prior predictive distributions

 

- Posterior predictive distributions

 

- Log-normal likelihood

 

 

 

 

 Wednesday - Classes from 9:30 to 17:00

 

 

 Bayesian regression models (Chapter 4 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

 

- Linear regression

 

- Log-normal model

 

- Logistic regression

 

 

 

 

Thursday - Classes from 9:30 to 17:00

 

 

Bayesian hierarchical models (Chapter 5 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

 

- Hierarchical normal model (Linear mixed models)

 

- Hierarchical log normal model

 

Optional:

 

- Distributional regression models

 

- Hierarchical logistic regression model

 

 

 

 

Friday - Classes from 9:30 to 17:00

 

 

Review and model comparison with Bayes factor (Part of Chapter 8 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

 

 

- Review

 

- Theory behind Bayes factor

 

- Bayes factor sensitivity to priors

 

- Some instructive case studies