Academic English in Statistics (2014 Fall)

General Information

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 2.4Cross-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