Libraries
1
Lecture 01: Golems, Owls and DAGs
1.1
Limitation of Statistical models
1.2
Null models rarely unique
1.3
Hypotheses and Models
1.4
Owls
1.4.1
Why drawing the Bayesian Owl
1.4.2
Five steps of
1.4.3
Advantages of Bayesian Owl
1.5
DAGs (Directed Acylcic Graphs)
1.5.1
Science before statistics
1.5.2
Causal inferences
1.5.3
Tips
2
Lecture 02
2.1
What proportion of the surface is covered with water?
2.2
Bayesian data analysis
2.3
Globe tossing
2.3.1
2.3.2
Letters from my reviewers
2.4
Formalities
2.5
Grid approximation
2.6
Reference
3
Lecture 05
3.1
Elemental confounds
3.2
The fork
3.2.1
Fork example
3.2.2
What does it mean to stratify by a continouse variable
3.2.3
3.3
Reference
4
Lecture 07
4.1
Copernican Model
4.2
Reference
5
Lecture 09
5.1
Generative model
5.2
5.3
Generalized Linear Models
5.4
Links and inverse links
5.5
Reference
6
Lecture 11
7
Lecture 12
Notes and assignments on Statistical Rethinking 2022
Chapter 7
Lecture 12