In statistics, spike-and-slab regression is a Bayesian variable selection technique that is particularly useful when the number of possible predictors is larger than the number of observations. Initially, the idea of the spike-and-slab model was proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005).