Bayesian statistics: Course notes

by Irving Gómez Méndez

These are the notes for the course of Bayesian statistics.

The codes are available in this Github repository.

Note

These notes is a work in progress.

Write me to

gomendez (dot) irving (at) gmail (dot) com

if you notice anything broken or any mistake.

I Philosophy and basics of Bayesian statistics

• 1. Bayesian philosophy
• 2. Uniparametric models
• 3. Conjugate analysis

II Multiparametric models and regression

• 4. Normal likelihood with mean and variance unknown
• 5. Bayesian regression

III Reference analysis and normal approximation

• 6. Reference analysis
• 7. Normal approximation

IV Bayesian inference

• 8. Grid approximation and statistical inference
• 9. Model checking
• 10. Bayesian regression (2)
• 11. Quadratic approximation and MCMC

V Causality

• 12. Introduction to causality
• 13. Categorical variables, multicollinearity and biases
• 14. \(d\)-separation, causal and noncausal paths

VI Hierarchical models, and evaluation and comparison of models

• 15. Hierarchical models
• 16. Evaluating and comparing models

• References