  Spike-and-slab priors are popular Bayesian solutions for high-dimensional
linear regression problems. Previous theoretical studies on spike-and-slab
methods focus on specific prior formulations and use prior-dependent conditions
and analyses, and thus can not be generalized directly. In this paper, we
propose a class of generic spike-and-slab priors and develop a unified
framework to rigorously assess their theoretical properties. Technically, we
provide general conditions under which generic spike-and-slab priors can
achieve the nearly-optimal posterior contraction rate and the model selection
consistency. Our results include those of Narisetty and He (2014) and Castillo
et al. (2015) as special cases.
