Lectures
- Lecture 1: Intro and Probability Review
- Lecture 2: Binomial Model
- Lecture 3: Poisson Model
- Lecture 4: Exponential Family and Monte Carlo
- Lecture 5: Two-parameter Models: Normal Model. Jeffreys Prior
- Lecture 6: Normal Model continued
- Lecture 7: Normal Model and Intro to Gibbs
- Lecture 8: Gibbs Sampling and MCMC diagnostics
- Lecture 9: Multivariate Normal Model. Wishart and Inverse-Wishart
- Lecture 10: Multivariate Normal Model continued
- Lecture 11: Group Comparisons and Hierarchical Models
- Lecture 12: Linear Regression Model
- Lecture 13: Linear Regression continued
- Lecture 14: Model Selection
- Lecture 15: Generalized Linear Models and Metropolis Hastings
- Lecture 16: Metropolis Algorithm (in progress)
- Lecture 17: Metropolis Hastings Algorithm and Correlated errors (in progress)
- Lecture 18: Linear and Generalized Linear Mixed effects models (in progress)
- Lecture 19: MCMC: why does it work? (in progress)
- Lecture 20: Latent variable methods and Rank Likelihood (in progress)
- Lecture 21: (in progress)