General Information

• Course Plan

Part I: General Topics

1. The way we read and present academic work
2. A brief language guide for statistical writing
3. The structure of statistical papers
4. Typesetting your manuscript

Part II: Statistical Topics

1. Bayesian Statistics (Reference Book: Bayesian Data Analysis)
   1. Lecture: Introduction to Bayesian Inference
   2. Lecture: Fundamentals of Bayesian Data Analysis
   3. Presentation Topic 1.3: Bayesian single-parameter models
   4. Presentation Topic 1.4: Bayesian hierarchical models
   5. Presentation Topic 1.5: Bayesian generalized linear models
   6. Presentation Topic 1.6: Bayesian models for missing data
   7. Presentation Topic 1.7: Bayesian models for social networks
2. Machine Learning (Reference Book: The Elements of Statistical Learning)
   1. Lecture: Linear Methods for Regression and Classification
   2. Lecture: Smoothing methods
   3. Lecture: Regression density estimation
   4. Presentation Topic 2.4: Cross-Validation
   5. Presentation Topic 2.5: Hierarchical Mixtures of Experts
   6. Presentation Topic 2.6: Generalized Additive Models
   7. Presentation Topic 2.7: Neural Networks
   8. Presentation Topic 2.8: Undirected Graphical Models
   9. Presentation Topic 2.9: High-Dimensional Regression
3. Statistical Simulation Methods (Reference Book: Bayesian Data Analysis)
   1. Lecture: Introduction to MCMC methods
   2. Lecture: Sequential Monte Carlo
   3. Lecture: Hamiltonian Monte Carlo
   4. Presentation Topic 3.4: The Gibbs Sampler
   5. Presentation Topic 3.5: Metropolis and Metropolis-Hastings algorithms
   6. Presentation Topic 3.6: MCMC Convergence and Convergence Diagnostics