[Video event 74] Bayesian Data Analysis with BRMS

[samvmdev]

Mitzi Morris: Bayesian Data Analysis with BRMS

## Timestamps
00:00 R Ladies NYC Intro
4:55 Data Umbrella Intro 
8:25 Speaker Introduction - Mitzi Morris
10:15 What is BRMS?
11:15 Three reasons to use BRMS
13:51 Bayesian Workflow Overview
15:25 Modeling Terminology and Notation
17:54 Multilevel Regression
21:30 Regression Models in R + brief recent history of Bayesian programming languages
27:22 Linear Regression
28:52 Generalized Linear Regression
31:05 Regression Formula Syntax in BRMS
34:33 BRMS Processing Steps
37:13 Notebook - link to online notebook and data
37:38 Demo - in Markdown (.rmd)
38:18 Load packages (readr, ggplot2, brms, bayesplot, loo, projprod, cmdstanr)
38:38 Book - ARM
39:07 Example - Multilevel hierarchical model (with EPA radon dataset)
40:32 Further description of radon
41:37 Regression model
42:02 Demo - data example
42:26 3 Modeling Choices
44:31 Choice 1 - Complete Pooling Model (simple linear regression formula)
48:22 Choice 2 - No Pooling Model (not ideal)
50:17 Choice 3 - Partial Pooling Model
56:26 Q&A - How to compare the different models? (run loo)
1:00:00 Q&A - Does BRMS have options for checking model assumptions?
1:01:00 Q&A What were the default priors? (student T-distribution with 3 degrees of freedom)
1:05:27 References

## Resources
- https://paul-buerkner.github.io/brms/articles/index.html 
- https://xcelab.net/rm/statistical-rethinking/
- https://journal.r-project.org/archive/2018/RJ-2018-017/RJ-2018-017.pdf 
- https://www.barelysignificant.com/slides/RGUG2019/#1
- https://ourcodingclub.github.io/tutorials/brms 
- https://onlinelibrary.wiley.com/doi/pdf/10.1111/eth.13225 
- https://mc-stan.org/users/documentation/case-studies/tutorial_rstanarm.html 

[reshamas]

Thank you @greenhacks. The timestamps have been added to the video and the file.