A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is
AbstractBayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response. The widespread use of this method has been restricted by the challenging computational problem of sampling from the corresponding posterior distribution. Recently, the use of adaptive Monte Carlo methods has been shown to lead to performance improvement over traditionally used algorithms in linear regression models. This paper looks at applyingdoi:10.1007/s11222-020-09974-2 fatcat:xequbxteo5a2hokbwff7jsq6oi