# Video: Bayesian Data Analysis with BRMS

**URL:** <https://discourse.mc-stan.org/t/video-bayesian-data-analysis-with-brms/30853>\
**Category:** Events\
**Tags:** brms\
**Created:** [March 23, 2023, 5:58pm UTC](https://discourse.mc-stan.org/t/video-bayesian-data-analysis-with-brms/30853 "2023-03-23T17:58:46Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![reshamas](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/reshamas/32/15277_2.png) [@reshamas](https://discourse.mc-stan.org/u/reshamas)\
**Post date:** [March 23, 2023, 5:58pm UTC](https://discourse.mc-stan.org/t/video-bayesian-data-analysis-with-brms/30853/1 "2023-03-23T17:58:46Z")

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Wanted to share a video of a recent presentation by Mitzi Morris (@mitzimorris)

## Bayesian Data Analysis with BRMS

BRMS=Bayesian Regression Models using Stan

💡Mitzi Morris shows how you can quickly build robust models for data analysis and prediction using BRMS. After a brief overview of the the advantages and limitations of BRMS and a quick review of multi-level regression. We will work through an R-markdown notebook together, to see how to fit, visualize, and test the goodness of the model and resulting estimates.

[![](https://img.youtube.com/vi/A1NWoKQhgJE/maxresdefault.jpg "[74] Bayesian Data Analysis with BRMS (Bayesian Regression Models Using Stan) (Mitzi Morris)") ](https://www.youtube.com/watch?v=A1NWoKQhgJE)

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**Author:** ![reshamas](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/reshamas/32/15277_2.png) [@reshamas](https://discourse.mc-stan.org/u/reshamas)\
**Post date:** [October 13, 2023, 3:02pm UTC](https://discourse.mc-stan.org/t/video-bayesian-data-analysis-with-brms/30853/2 "2023-10-13T15:02:50Z")

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Please note that timestamps and a full transcript are now available for:  
[Video: Bayesian Data Analysis with BRMS (Mitzi Morris, @mitzimorris)](https://youtu.be/A1NWoKQhgJE)

## Full transcript

> **[Bayesian Data Analysis with BRMS (Bayesian Regression Models Using Stan) –...](https://blog.dataumbrella.org/mitzi-brms)**
>
> Quickly build robust models for data analysis and prediction using BRMS

## Timestamps

00:00 R-Ladies NYC Intro  
04:55 Data Umbrella Intro  
08:25 Speaker Introduction - Mitzi Morris  
10:15 What is BRMS? (Bayesian Regression Models Using Stan)  
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)  
01:00:00 Q&A - Does BRMS have options for checking model assumptions?  
01:01:00 Q&A What were the default priors? (student T-distribution with 3 degrees of freedom)  
01:05:27 References

## About the Speaker

Mitzi Morris is a member of the Stan Development Team and serves on the Stan Governing Body. Since 2017 she has been a full-time Stan developer, working for Professor Andrew Gelman at Columbia University, where she has contributed to the core Stan C++ platform and developed CmdStanPy, a modern Python interface for Stan. She is also as an active Stan user, developing, publishing, and presenting on Bayesian models for disease mapping. Prior to that she has worked as a software engineer in both academia and industry, working on natural language processing and search applications as well as data analysis pipelines for genomics and bioinformatics.

### Connect with the Speaker

- GitHub: @mitzimorris

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**Author:** ![mitzimorris](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/mitzimorris/32/29_2.png) [@mitzimorris](https://discourse.mc-stan.org/u/mitzimorris)\
**Post date:** [October 13, 2023, 4:41pm UTC](https://discourse.mc-stan.org/t/video-bayesian-data-analysis-with-brms/30853/3 "2023-10-13T16:41:14Z")

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Many thanks for adding timestamps - extremely useful.

Materials for an expanded version of this tutorial that I gave at StanCon 2023 are here: [StanCon 2023 Tutorial on BRMS](https://github.com/stan-dev/stancon2023/tree/main/Mitzi-Morris#notebooks-and-data-for-stancon-2023-tutorial-on-brms)