The SSVS prior is used for variable selection, which is conceptually different from model averaging, right? However, both result in coefficient estimates and posterior inclusion probabilities and are often mentioned together in literature (see for instance the article on BMA linked above).
Can anyone provide more systematic insights regarding the relationship of SSVS and BMA? Where do they differ? Where are similarities to be found? Do they generally give similar results? Is the model space they explore the same?