Hierarchical sparsity priors for regression models

摘要: Sparse regression problems, where it is usually assumed that there are many variables and the effects of a large subset negligible, have become increasingly important. This paper describes construction hierarchical prior distributions when considered related. These priors allow dependence between coefficients shrinkage to zero different be The properties these discussed applications linear models with interactions generalized additive used as illustrations.