Notes
- Lecture 1: Course Overview: (PDF) (R Markdown) (KEY)
- Lecture 2: Credibility, Models, Parameters (PDF) (R Markdown) (KEY) (VIDEO - Jan 16)
- Lecture 3: Probability, Part 1 (PDF) (R Markdown) (KEY)
- Lecture 4: Probability, Part 2 (PDF) (R Markdown) (KEY)
- Lecture 5: Bayes Rule (PDF) (R Markdown) (KEY)
- Lecture 6: Binomial Probability (PDF) (R Markdown) (KEY)
- Lecture 7: MCMC and JAGS (PDF) (R Markdown) (KEY)
- Lecture 8: Normal Model (PDF) (R Markdown) (KEY)
- Lecture 9: T-distribution (PDF) (R Markdown) (KEY)
- Lecture 10: Hierarchical Models (PDF) (R Markdown) (KEY)
- Lecture 11: Generalized Linear Models (PDF) (R Markdown) (KEY)
- Lecture 12: Linear Regression (PDF) (R Markdown) (KEY)
- Lecture 13: Binary and Count Regression (PDF) (R Markdown) (KEY)