15 citations · 37 across the 4 of their papers we have counts for
4 papers · 1 filter
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Yabo Niu, Yang Ni, Debdeep Pati +1
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there i…
Robust probabilistic inference via a constrained transport metric
Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
Flexible Bayesian models are typically constructed using limits of large parametric models with a multitude of parameters that are often uninterpretable. In this article, we offer…
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
Sutanoy Dasgupta, Peng Zhao, Jacob Helwig +3
Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-li…
A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models
Gautam Sabnis, Debdeep Pati, Barbara Engelhardt +1
We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian fact…