4 papers · 1 filter
Multiple testing with the horseshoe
Sayantan Banerjee, Ismaël Castillo, Fanny Villers
We study multiple testing under continuous global--local shrinkage priors, with a focus on the horseshoe prior in high-dimensional sparse settings. While such priors provide adapti…
Bayesian inference in high-dimensional models
Sayantan Banerjee, Ismaël Castillo, Subhashis Ghosal
Models with dimension more than the available sample size are now commonly used in various applications. A sensible inference is possible using a lower-dimensional structure. In re…
Deep Horseshoe Gaussian Processes
Ismaël Castillo, Thibault Randrianarisoa
Deep Gaussian processes have recently been proposed as natural objects to fit, similarly to deep neural networks, possibly complex features present in modern data samples, such as…
Heavy-tailed Bayesian nonparametric adaptation
Sergios Agapiou, Ismaël Castillo
We propose a new Bayesian strategy for adaptation to smoothness in nonparametric models based on heavy tailed series priors. We illustrate it in a variety of settings, showing in p…