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In Bayesian statistics, stochastic search variable selection refers to a Gibbs sampling approach that explores subsets of predictors by giving each coefficient a two-component normal mixture prior and visiting promising models more often. This meaning comes from George (1993) in Journal of the American Statistical Association.[1]
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