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Enrollment is Closed


This course provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Knowledge of algebra and basic calculus is a prerequisite.


  1. Introduction: Credibility, Models, and Parameters
  2. The R Programming Language
  3. What Is This Stuff Called Probability?
  4. Bayes’ Rule
  5. Inferring a Binomial Probability via Exact Mathematical Analysis
  6. Markov Chain Monte Carlo
  7. JAGS
  8. Hierarchical Models
  9. Model Comparison and Hierarchical Modeling
  10. Null Hypothesis Significance Testing
  11. Bayesian Approaches to Testing a Point (“Null”) Hypothesis
  12. Goals, Power, and Sample Size
  13. Stan
  14. Overview of the Generalized Linear Model
  15. Metric-Predicted Variable on One or Two Groups