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 (to appear 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.