6 papers
E-Values For Multiplicity Control In Multiverse Analysis
Paul Rognon-Vael, David Rossell
Multiverse analysis refers to a common situation where one wishes to assess the association between multiple possible treatment definitions and multiple possible outcome definition…
Positive-definiteness in separable priors: effects on prior interpretability and inference
Jack Storror Carter, David Rossell
A popular class of priors for symmetric positive-definite matrices assumes independent entries and adds a truncation to ensure positive-definiteness. While conceptually simple and…
Bayesian computation for high-dimensional Gaussian Graphical Models with spike-and-slab priors
Deborah Sulem, Jack Jewson, David Rossell
Gaussian graphical models are widely used to infer dependence structures. Bayesian methods are appealing to quantify uncertainty associated with structural learning, i.e., the plau…
Empirical Bayes for Data Integration
Paul Rognon-Vael, David Rossell
We discuss the use of empirical Bayes for data integration, in the sense of transfer learning. Our main interest is in settings where one wishes to learn structure (e.g. feature se…
Improving variable selection properties with data integration and transfer learning
Paul Rognon-Vael, David Rossell, Piotr Zwiernik
We study variable selection (also called support recovery) in high-dimensional sparse linear regression when one has external information on which variables are likely to be associ…
Scalable Bayesian variable selection and model averaging under block orthogonal design
Omiros Papaspiliopoulos, David Rossell
We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal,…