The BDA_R_demos repository contains some R demos and additional notes for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3). See also Bayesian Data Analysis course material.

Currently there are demos for BDA3 Chapters 2, 3, 4, 5, 6, 10, 11 and 12. Furthermore there are demos for CmdStanR, RStan RStanARM.

The initial demos were originally written for Matlab by Aki Vehtari and translated to R by Markus Paasiniemi. Recently more demos have been added for CmdStanR, RStan and RStanARM. Unless otherwise specified in specific files all code licensed under BSD-3 and all text, slides and figures licensed under CC-BY-NC 4.0.

The corresponding Python demos and the corresponding Matlab/Octave demos.

See also Model Selection tutorial.

List of demos (not including rstan and rstanarm demos)

• Chapter 2
  • demo2_1: Probability of a girl birth given placenta previa (BDA3 p. 37)
  • demo2_2: Illustrate the effect of prior in binomial model
  • demo2_3: Illustrate simulation based inference
  • demo2_4: Illustrate grid and inverse-cdf sampling
• Chapter 3
  • demo3_1_4: Normal model with unknown mean and variance (BDA3 section 3.2 on p. 64)
  • demo3_5: Estimating the speed of light using normal model BDA3 p. 66
  • demo3_6: Binomial regression and grid sampling with bioassay data (BDA3 p. 74-)
• Chapter 4
  • demo4_1: Normal approximation for binomial regression model and Bioassay data
• Chapter 5
  • demo5_1: Hierarchical model for Rats experiment (BDA3, p. 102)
  • demo5_2: Hierarchical model for SAT-example data (BDA3, p. 102)
• Chapter 6
  • demo6_1: Posterior predictive checking of normal model for light data
  • demo6_2: Posterior predictive checking for independence in binomial trials
  • demo6_3: Posterior predictive checking of normal model with poor test statistic
  • demo6_4: Marginal posterior predictive checking with PIT test
• Chapter 7
  • See model selection tutorial
• Chapter 10
  • demo10_1: Rejection sampling
  • demo10_2: Importance sampling
  • demo10_3: Importance sampling with normal distribution as a proposal for Bioassay model
• Chapter 11
  • demo11_1: Gibbs sampling illustration
  • demo11_2: Metropolis sampling + convergence illustration
  • demo11_3_4: Metropolis sampling + convergence illustration
  • demo11_5: Diagnostics with posterior and bayesplot packages
• Chapter 12
  • demo12_1: Static Hamiltonian Monte Carlo illustration
  • demo12_2: NUTS / Dynamic Hamiltonian Monte Carlo illustration
• CmdStanR, RStan, RStanARM