Bayesian Model Averaging using Bayesian Adaptive Sampling
Description
Package for Bayesian Model Averaging in linear models
using stochastic or deterministic sampling without replacement
from posterior distributions. Prior distributions on
coefficients are from Zellner's g-prior or mixtures of g-priors
corresponding to the Zellner-Siow Cauchy Priors or the Liang et
al hyper-g priors (JASA 2008). Other model selection criterian
include AIC and BIC. Sampling probabilities may be updated
based on the sampled models. Allows uniform or beta-binomial
prior distributions on models.