Bayesian Variable Selection with Hierarchical Priors
Compute Differences Between MCMC Chains
Simulate Data
Create Groups of Covariates
Create Design Matrix With Orthogonal Columns
Sample Data
Extract Posterior Statistics
Print ptycho Object
Bayesian Variable Selection with Hierarchical Priors
Sample From Posterior Distributions
Identify Columns Containing Indicator Variables
Bayesian variable selection for linear regression models using hierarchical priors. There is a prior that combines information across responses and one that combines information across covariates, as well as a standard spike and slab prior for comparison. An MCMC samples from the marginal posterior distribution for the 0-1 variables indicating if each covariate belongs to the model for each response.