Large-Scale Bayesian Variable Selection Using Variational
Methods
Description
Fast algorithms for fitting Bayesian variable selection
models and computing Bayes factors, in which the outcome (or
response variable) is modeled using a linear regression or a
logistic regression. The algorithms are based on the variational
approximations described in "Scalable variational inference for
Bayesian variable selection in regression, and its accuracy in
genetic association studies" (P. Carbonetto & M. Stephens, 2012,
). This software has been applied to large
data sets with over a million variables and thousands of samples.
