30 citations · 119 across the 39 of their papers we have counts for
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Scalable Approximations of Marginal Posteriors in Variable Selection
Willem van den Boom, Galen Reeves, David B. Dunson
In many contexts, there is interest in selecting the most important variables from a very large collection, commonly referred to as support recovery or variable, feature or subset…
Robust Bayesian inference via coarsening
Jeffrey W. Miller, David B. Dunson
The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this…
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Ye Wang, David B. Dunson
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a l…
Semiparametric Bernstein-von Mises Theorem: Second Order Studies
Yun Yang, Guang Cheng, David B. Dunson
The major goal of this paper is to study the second order frequentist properties of the marginal posterior distribution of the parametric component in semiparametric Bayesian model…
On the consistency theory of high dimensional variable screening
Xiangyu Wang, Chenlei Leng, David B. Dunson
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection. As a preselection method, it selects a moderate size subset of candidat…