Spike and Slab

Spike-and-slab methods are Bayesian techniques primarily used for variable selection and model uncertainty quantification, achieving sparsity by assigning variables to either a "spike" (representing zero effect) or a "slab" (representing a non-zero effect) distribution. Current research focuses on extending these methods to handle high-dimensional data and complex relationships, including developing scalable algorithms like Gibbs sampling and incorporating spike-and-slab priors into more sophisticated models such as Gaussian processes and kernel regression for applications like transfer learning and equation discovery. This work is significant because it improves the efficiency and applicability of Bayesian variable selection, leading to more robust and interpretable models across diverse scientific fields and practical applications, such as improved decision-making in reinforcement learning and more accurate equation discovery from noisy data.