68 citations · 319 across the 71 of their papers we have counts for
7 papers · 1 filter
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…
Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels
Vishesh Karwa, Debdeep Pati, Sonja Petrović +6
We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic bl…
Bayesian fractional posteriors
Anirban Bhattacharya, Debdeep Pati, Yun Yang
We consider the fractional posterior distribution that is obtained by updating a prior distribution via Bayes theorem with a fractional likelihood function, a usual likelihood func…
Sparse additive Gaussian process with soft interactions
Garret Vo, Debdeep Pati
Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex.…
Sub-optimality of some continuous shrinkage priors
Anirban Bhattacharya, David B. Dunson, Debdeep Pati +1
Two-component mixture priors provide a traditional way to induce sparsity in high-dimensional Bayes models. However, several aspects of such a prior, including computational comple…
Bayesian Variable Selection for Skewed Heteroscedastic Response
Libo Wang, Yuanyuan Tang, Debajyoti Sinha +2
In this article, we propose new Bayesian methods for selecting and estimating a sparse coefficient vector for skewed heteroscedastic response. Our novel Bayesian procedures effecti…
Probabilistic community detection with unknown number of communities
Junxian Geng, Anirban Bhattacharya, Debdeep Pati
A fundamental problem in network analysis is clustering the nodes into groups which share a similar connectivity pattern. Existing algorithms for community detection assume the kno…