Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables $(x_{1},\ldots ,x_{K})$ is possibly much larger than the ...

High-dimensional feature screening and variable selection represent critical methodological advancements designed to address the challenges posed by datasets where the number of potential predictors ...

Up to now, it has taken a great deal of computational effort to detect dependencies between more than two high-dimensional variables, in particular when complicated non-linear relationships are ...