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

15 thoughts on “Academic English in Statistics (2014 Fall)”

  1. Dear professor Li,our group decided 3.6: MCMC Convergence and Convergence Diagnostics as our presentation topic.May the God blessing us and a good night to you.

  2. Dear professor Li,our group decided 3.5: Metropolis and Metropolis-Hastings algorithms as our presentation topic.May the God blessing us and a good night to you.

  3. Dear professor Li,our group of three(Xiaoshen Li,Hao Zhang,Yao Yi)decided”1.4: Bayesian hierarchical models”as our presentation topic.May the God blessing us and a good night to you.

  4. We choose Presentation 1.3: Bayesian single-parameter models.We would try our best to prepare it.Thank you for searching so many topics for us.Have a good time in the National Day holiday,my dear teacher!

  5. Dear teacher Feng,as our topic has been chosen,we would like to choose another one.The topic we prefer is topic 1.5: Bayesian generalized linear models. We will do our best!
    Having a nice holiday!

  6. We choose the topic 2.5: Hierarchical Mixtures of Experts and we will try our best to finish it.Thank you.
    Best wishes.

  7. We choose the topic2.4:Cross-Validation for our presentation.We will prepare for it carefully.Thank you so much,have a nice week!

  8. We choose the topic 2.7:Neural Networks for our presentation. We will do our best!
    Thank you,enjoy your holidays!O(∩_∩)O~

  9. We choose the topic 2.9: High-Dimensional Regression
    We will try our best to prepare the presentation.
    Thank you, have a happy holidays!

  10. We choose the topic 2.7:Neural Networks for our presentation.We will prepare it adequately.
    Thank you!
    Best wishes for you!

  11. We choose the topic 1.7: Bayesian models for social networks for our presentation.We pray we can get this topic.
    Thank you.Happy holidays!

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