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04-learning-more.Rpres
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04-learning-more.Rpres
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Bayesian Regression Models with RStanARM
========================================================
author: TJ Mahr
date: Sept. 21, 2016
autosize: true
incremental: false
Madison R Users Group
<small>
[Github repository](https://github.com/tjmahr/MadR_RStanARM)
<br/>
[@tjmahr](https://twitter.com/tjmahr)
</small>
Overview
===============================================================================
- [How I got into Bayesian statistics](http://rpubs.com/tjmahr/rep-crisis)
- [Some intuition-building about Bayes theorem](http://rpubs.com/tjmahr/bayes-theorem)
- [Tour of RStanARM](http://rpubs.com/tjmahr/rstanarm-tour)
- **[Where to learn more about Bayesian statistics](http://rpubs.com/tjmahr/bayes-learn-more)**
Learning more
===============================================================================
type: section
You want to learn RStanARM
===============================================================================
See the [vignettes](https://cran.rstudio.com/web/packages/rstanarm/). There is one for each kind of model.
You want to learn Stan
===============================================================================
- Go to <http://mc-stan.org/documentation/>
- Look at the [example models](https://github.com/stan-dev/example-models/wiki) and read the manual.
The Stan Manual
==============================================================================
***
You are trained in classical regression
===============================================================================
left: 70%
Read [_Statistical Rethinking_](http://xcelab.net/rm/statistical-rethinking/) and [watch the lectures](https://www.youtube.com/playlist?list=PLDcUM9US4XdMdZOhJWJJD4mDBMnbTWw_z).
***
'Rethinking'
===============================================================================
* This book is exceptional, stuffed to the brim with trivia, advice, and wisdom.
* Not just a book about statistics, but how we use statistical models in scientific practice.
* Explains side issues like MCMC, basics of information theory, or why the normal distribution is so prevalent -- but just deep enough for the reader to get the intuitions needed for practice.
* My sole criticism is that its companion R `rethinking` package is not on CRAN.
Rethinking 1
===============================================================================
title:false
Rethinking 2
===============================================================================
title:false
***
You need puppies on the cover
===============================================================================
Kruschke (2015). [_Doing Bayesian Analysis_](https://sites.google.com/site/doingbayesiandataanalysis/what-s-new-in-2nd-ed).
I just got this book, but so far it's approachable and comprehensive, like a statistical cookbook. Many more equations and proofs than in _Rethinking_.
***
Puppies
===============================================================================
title:false
***
You study psycholinguistics
===============================================================================
left: 60%
* Nicenboim and Vasishth (2016). Statistical methods for linguistic research: Foundational Ideas - Part II. <https://arxiv.org/abs/1602.00245>
* Sorensen and Vasishth (2016). Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists. <https://arxiv.org/abs/1506.06201>
***
You need a reading list and fast
===============================================================================
* Etz, et al. (2016). [How to become a Bayesian in eight easy steps: An annotated reading list](https://www.researchgate.net/publication/301981861_How_to_become_a_Bayesian_in_eight_easy_steps_An_annotated_reading_list).
* Etz has also [great tutorials](https://alexanderetz.com/understanding-bayes/). They focus mostly on Bayes factors, a way to compare models using the denominators from Bayes theorem. _Statistical Rethinking_ mentions these in passing in a single paragraph.
You need to figure out how many socks are in the laundry
==============================================================================
- See [Rasmus Bååth's intro to approximate Bayesian inference](http://www.sumsar.net/blog/2015/07/tiny-data-and-the-socks-of-karl-broman-the-movie/).
- Kind of removed from our topic, but entertaining example that emphasizes how Bayesian models are generative.
- If we are exploring all the plausible ways the data could have been generated, we're probably doing a kind of Bayesian analysis.