Skip to main content

Ctrl+K

Bayesian statistics: Course notes

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

Search

Ctrl+K

By Irving Gómez Méndez

© Copyright 2022.