15 citations · 21 across the 4 of their papers we have counts for
6 papers · 1 filter
Merging Two Cultures: Deep and Statistical Learning
Anindya Bhadra, Jyotishka Datta, Nick Polson +2
Merging the two cultures of deep and statistical learning provides insights into structured high-dimensional data. Traditional statistical modeling is still a dominant strategy for…
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Yabo Niu, Nilabja Guha, Debkumar De +3
Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorpo…
Horseshoe Regularization for Machine Learning in Complex and Deep Models
Anindya Bhadra, Jyotishka Datta, Yunfan Li +1
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machin…
Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis
Yunfan Li, Jyotishka Datta, Bruce A. Craig +1
Seemingly unrelated regression is a natural framework for regressing multiple correlated responses on multiple predictors. The model is very flexible, with multiple linear regressi…
Divide and Recombine for Large and Complex Data: Model Likelihood Functions using MCMC
Qi Liu, Anindya Bhadra, William S. Cleveland
In Divide & Recombine (D&R), big data are divided into subsets, each analytic method is applied to subsets, and the outputs are recombined. This enables deep analysis and practical…
Inferring network structure in non-normal and mixed discrete-continuous genomic data
Anindya Bhadra, Arvind Rao, Veerabhadran Baladandayuthapani
Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are poten…