# SCS Reads 2016-2017

### From Wiki1

## Contents |

## Getting started

We chose to read McElreath's Statistical Rethinking.

- McElreath's Statistical Rethinking page contains a lot of useful information
- All the code examples from the text
- lecture slides that follow the text

- To usefully work along in R with the text, you'll need to install the rethinking package. But this requires RStan, which in turn requires the Rtools development software. You need to do this in the order `Rtools -> RStan -> rethinking`.
- RStan Getting Started describes the steps to install Rtools first and then RStan
- McElreath's software page describes installing the rethinking package. It is best to do this from the github source package as he describes.

## Meetings

### Week 1, Sept. 23

- McElreath, Chapter 1

Another paper drawing an analogy between statistical modeling and engineering (and art): File:Thissen2001.pdf

### Week 2, Oct. 7

- McElreath, Chapter 2

Answers to some exercises from Ch. 2 (and corresponding RMarkdown file).

### Week 3, Oct. 21

- McElreath, Chapter 3 and Chapter 4 through p. 91

Answers to some exercises from Ch. 3 (and corresponding RMarkdown file).

### Week 4, Nov. 4

- McElreath, remainder of Chapter 4 and all of Chapter 5

- Answers to some exercises from Ch. 4 (and corresponding RMarkdown file).

- Answers to some exercises from Ch. 5 (and corresponding RMarkdown file).

- Regarding better plots for parameter estimates in Bayes: This Paper by Kay, Nelson & Hekler, "Researcher-Centered Design of Statistics: Why Bayesian Statistics Better Fit the Culture and Incentives of HCI" uses what I mentioned as violin plots to show posterior densities of parameters. It is worth reading for the comparison of frequentist and Bayesian approaches.

- Matthew Kay also has developed a tidybayes R package for composing/extracting tidy data from Bayesian samplers. Only on github, and no real examples, but it seems promising.

### Week 5, Nov. 18

- McElreath, Chapter 6

- Answers to some exercises from Ch. 6 (and corresponding RMarkdown file).

### Week 6, Dec. 2

- McElreath, Chapter 8

- MCMC Examples (and corresponding
**knitr**LaTeX and BiBTeX files).

- Answers to some exercises from Ch. 8 (and corresponding RMarkdown file).

### Week 7, January 13

- McElreath, Chapter 7

### Week 8, January 27

- McElreath, Chapter 9

### Week 8.5, February 3

We thought that we'd view and discuss John Oliver's Last Week Tonight Show on 'Scientific Studies', originally aired in May 2016.

Of related interest is a recent (January 16, 2017) article in the "The Upshot": How to Prevent Whiplash From Ever-Changing Medical Advice

### Week 9, February 10

- McElreath, Chapter 10

### Week 10, March 3

- McElreath, Chapter 11

- Friendly & Meyer (2016), File:DDAR-Ch11.pdf on count data regression models

### Week 11, March 17

- McElreath, Chapter 12

### Week 12, March 31

- McElreath, Chapters 13 and 14

## Candidates

### **Andrew Gelman et al. (2014) Bayesian Data Analysis, 3rd edition**

- Amazon link to Gelman et al. BDA3
- The Amazon preview provides access to the table of contents and to many of the earlier pages in the book.

- The home page for BDA3 has datasets, lecture slides, some solutions, etc.
- My opinion: Lots of exercises with a range of difficulty. For some, a current introductory 'bible' on
*applied*bayesian data analysis with solid theoretical content. I think that in one year we can get far enough to learn how to use Hamiltonian Monte Carlo with STAN in R.

### **Richard McElreath (2016) Statistical Rethinking: A Bayesian course with examples in R and STAN.**

- Recommended by Heather. This might be the 'best new book' on Bayesian data analysis. Fewer equations and less theory than BDA3 but, instead of formulas, the book has short snippets in R that you run to illustrate concepts. It doesn't go as far as BDA3, e.g. no non-linear and non-parametric chapters, but has many observations (the ones I have read so far are sound in my opinion) about statistical methodology and connections between bayesian and frequentist concepts -- although the author suggests that he would have liked to have done more but didn't want to crowd out the bayesian material. Here's an excerpt to illustrate the author's point of view:
- As a consequence, this book doesn't argue against p-values and the like. The problem in my opinion isn't so much p-values as the set of odd rituals that have evolved around them, in the wilds of the sciences, as well as the exclusion of so many other useful tools.

- Lots of R code (pre-Wickham in style), lots of exercises conveniently labelled 'Easy', 'Medium' and 'Hard'.
- Here's a photograph of a page to give an idea of the style of the book.

- Web sites for the book:
- McElreath's web site listing software related to the book
- The GitHub repository for the `rethinking` R package. A quick look here shows that the package includes a large number of data sets and functions for doing the analyses and graphs described in the book.

- Some comments in blogs:
- Christian Robert's (a major collaborator with Gelman) blog
- Andrew Gelman's blog
- Rasmus Baath, a young Swedish statistician, called it a 'pedagogical masterpiece' in his Amazon customer review. He also gave Krushke's Doing Bayesian Data Analysis a glowing review when he reviewed it in 2012.

- Ordering info:

- Opinions:

- I like the overall flavor of what I've seen of this book. (M. F.)