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## General Information

## Part I: General Topics

- The way we read and present academic work
- A brief language guide for statistical writing
- The structure of statistical papers
- Typesetting your manuscript

## Part II: Statistical Topics

- Bayesian Statistics (Reference Book: Bayesian Data Analysis)
- Lecture: Introduction to Bayesian Inference
- Lecture: Fundamentals of Bayesian Data Analysis
- Presentation Topic 1.3: Bayesian single-parameter models
- Presentation Topic 1.4: Bayesian hierarchical models
- Presentation Topic 1.5: Bayesian generalized linear models
- Presentation Topic 1.6: Bayesian models for missing data
- Presentation Topic 1.7: Bayesian models for social networks

- Machine Learning (Reference Book: The Elements of Statistical Learning)
- Lecture: Linear Methods for Regression and Classification
- Lecture: Smoothing methods
- Lecture: Regression density estimation
- Presentation Topic 2.4: Cross-Validation
- Presentation Topic 2.5: Hierarchical Mixtures of Experts
- Presentation Topic 2.6: Generalized Additive Models
- Presentation Topic 2.7: Neural Networks
- Presentation Topic 2.8: Undirected Graphical Models
- Presentation Topic 2.9: High-Dimensional Regression

- Statistical Simulation Methods (Reference Book: Bayesian Data Analysis)
- Lecture: Introduction to MCMC methods
- Lecture: Sequential Monte Carlo
- Lecture: Hamiltonian Monte Carlo
- Presentation Topic 3.4: The Gibbs Sampler
- Presentation Topic 3.5: Metropolis and Metropolis-Hastings algorithms
- Presentation Topic 3.6: MCMC Convergence and Convergence Diagnostics

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

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.

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.

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.

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!

We choose the topic 3.4: The Gibbs Sampler. we think we will do it well.

Thank you.Happy National Day!

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!

Good day, sir. We decide to choose the topic 2.8: Undirected Graphical Models. We think we can do it well. Hope you’ll accept.

Thank you.

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

Best wishes.

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

We choose the topic 2.6: Generalized Additive Models.

Thank you .Best wishes!

We choose the topic 2.7:Neural Networks for our presentation. We will do our best!

Thank you,enjoy your holidays！O(∩_∩)O~

We choose the topic 2.9: High-Dimensional Regression

We will try our best to prepare the presentation.

Thank you, have a happy holidays!

We choose the topic 2.7:Neural Networks for our presentation.We will prepare it adequately.

Thank you!

Best wishes for you!

Presentation Topic 1.6: Bayesian models for missing data

We’ll our best for this presentation.

Happy everyday.

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！