Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
Sparsity Information and Regularization in the Horseshoe and other Shrinkage Priors , volume=
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PliableBVS is a new Bayesian hierarchical spike-and-slab model for simultaneous selection of high-dimensional main effects and interactions under an asymmetric weak hierarchical constraint, shown to outperform pliable lasso in simulations.
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To select or not to select: predictively consistent priors instead of model selection
Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
PliableBVS is a new Bayesian hierarchical spike-and-slab model for simultaneous selection of high-dimensional main effects and interactions under an asymmetric weak hierarchical constraint, shown to outperform pliable lasso in simulations.