• Preface
    • Prerequisites
    • Going Further
    • Note
    • About the Author
  • Introduction
    • Bayesian Probability
      • Conditional probability & Bayes theorem
  • A Hands-on Example
    • Prior, likelihood, & posterior distributions
    • Prior
    • Likelihood
    • Posterior
    • Posterior predictive
  • Regression Models
    • Example: Linear Regression Model
      • Setup
      • Stan Code
      • Running the Model
  • Model Exploration
    • Monitoring Convergence
      • Visual Inspection: Traceplot & Densities
      • Statistical Measures
      • Autocorrelation
    • Model Criticism
      • Sensitivity Analysis
      • Predictive Accuracy & Model Comparison
      • Posterior Predictive Checking: Statistical
      • Posterior Predictive Checking: Graphical
    • Summary
  • Model Enhancements
    • Generating New Variables of Interest
    • Robust Regression
    • Generalized Linear Model
  • Issues
    • Debugging
    • Choice of Prior
      • Noninformative, Weakly Informative, Informative
      • Conjugacy
      • Test your Priors Beforehand
      • Hierarchical Priors
      • Sensitivity Analysis Revisited
      • A Simple Check
      • Summary
    • Sampling Procedure
      • Metropolis
      • Gibbs
      • Hamiltonian Monte Carlo
      • Other Variations and Approximate Methods
    • Number of draws, thinning, warm-up
    • Model Complexity
  • R Packages
    • Standard Regression and GLM
    • Categorical Models
    • Extended Count Models
    • Mixed Models
    • Other Models and Related
    • Even More Packages
  • Final Thoughts
  • Appendix
    • Maximum Likelihood Review
      • Example
    • Linear Model
    • Binomial Likelihood Example
    • Modeling Languages
      • Bugs
      • JAGS
      • Nimble
      • Stan
      • R
      • General Statistical Packages
      • Other Programming Languages
      • Summary
    • BUGS Example
    • JAGS Example
    • Metropolis Hastings Example
    • Hamiltonian Monte Carlo Example
  • References
    • Texts for Your Shelf
    • Stan Specific Resources
    • Works Cited/Used
  • MC logo

Bayesian Basics

Bayesian Basics

Michael Clark
m-clark.github.io