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.
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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)
Full transcript
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
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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
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