Bayesian Bootstrap Spike-and-Slab LASSO

Understand

The impracticality of posterior sampling has prevented the widespread adoption of spike-and-slab priors in high-dimensional applications.

• To alleviate the computational burden, optimization strategies have been proposed that quickly find local posterior modes.
• Trading off uncertainty quantification for computational speed, these strategies have enabled spike-and-slab deployments at scales that would be previously unfeasible.
• We build on one recent development in this strand of work: the Spike-and-Slab LASSO procedure of Ro\v{c}kov\'{a} and George (2018).