BSSVS is traditionally applied to model selection problems in a linear regression framework, in which statisticians start with a large number of potential predictors X 1 , . . . , X P and ask which among these associate linearly with an N-dimensional outcome variable Y. For example, the full model becomes Y~½X 1 , . . . , X P bze, where b is a P-dimensional vector of regression coefficients and e is an N-dimensional vector of normally distributed errors with mean 0. When b p for p~1, . . . , P differs significantly from 0, X p helps predict Y, otherwise X p contributes little additional information and warrants removal from the model via forcing b p~0 . Given potentially high correlation between the predictors, deterministic model search strategies tend not to find the optimal set of predictors unless one explores all possible subsets. This exploration is generally computationally impractical as there exist 2 P such subsets and completely fails for PwN.