Harnessing Spike-And-Slab Priors For Sparse Modeling And Variable Selection
In Bayesian inference, spike-and-slab priors induce sparsity in models with numerous features and infrequent nonzero coefficients. They are hierarchical priors that use indicator variables to represent binary choices between active and inactive features. By integrating over these choices, spike-and-slab priors allow for efficient Gibbs sampling. They are commonly applied in variable selection, model averaging, and…