Conclusion

Bayesian Regression Modeling with rstanarm

Jake Thompson

Psychometrician, ATLAS, University of Kansas

What we've learned

• How to estimate a Bayesian regression model
  • Differences betweens frequentist and Bayesian approaches
  • Importance of making correct inferences
• Modifying a Bayesian model
  • Size of the posterior distribution
  • Prior distributions
  • Estimation algorithm

What we've learned

• Evaluate model fit
  • R-squared
  • Posterior predictive model checks
  • Model comparisons
• Using the model
  • Model visualizations
  • Predictions

What we've missed

• Math behind posterior calculations and LOO approximation
• Choosing a prior distribution
• Causes of estimation errors

What comes next?

• More DataCamp courses
  • Bayesian Modeling with RJAGS
• rstanarm documentation
  • mc-stan.org/rstanarm
• Bayesian Data Analysis, Gelman et al., (2013)

Thank you!

Bayesian Regression Modeling with rstanarm