Easy Bayes
Introduction
Overview
Goals
Prerequisites
Part I: Getting Started
Basic Bayesian Analysis
Advantages
Stan and the Stan ecosystem
Stan
rstan
rstanarm
brms
More Stan
Part II: rstanarm
Getting Started with rstanarm
Basic GLM
Traditional GLM
rstanarm: GLM
Adding more options
rstanarm: Mixed Model
rstanarm: Other Models
Priors
Default priors
Getting priors
Setting priors
Example
Part III: brms
Installing brms
Comparison to rstanarm
Models
Methods for brmsfit objects
Models in brms
brms: Mixed Model
brms: Mixed Model Extensions
brms: Mo’ models!
Part IV: Model Criticism
Model Criticism in rstanarm and brms
Model Exploration
Linear models
Marginal effects
Hypothesis tests
Extracting results
Tidy methods for data extraction
tidybayes
Model Diagnostics
shinystan
Posterior Predictive Checks
Observation Level
Model Performance
Prediction
Model Comparison
Model Averaging
Part V: Conclusion
Summary
Exercise
References
Easy Bayes with rstanarm and brms
Prediction
The usual methods of
fitted
and
predict
can be used
summary
raw