• 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
  • MC logo

Easy Bayes with rstanarm and brms

Easy Bayes with rstanarm and brms

Michael Clark m-clark.github.io University of Michigan: CSCAR University of Michigan: Advanced Research Computing

2018-11-15