68 citations · 353 across the 57 of their papers we have counts for
20 papers · 1 filter
Calibration-Free Robust Bayesian Inference for Discrete Unnormalized Models
Jiongran Wang, Debdeep Pati, Anirban Bhattacharya
Bayesian inference for discrete models with intractable normalizing constants is computationally challenging, particularly when the sample space is large or countably infinite. Alt…
Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data
Jiongran Wang, Debdeep Pati, Anirban Bhattacharya
Many statistical models involve parameter-dependent normalizing constants that are computationally intractable, creating substantial obstacles to standard Bayesian inference. Altho…
Robust Simulation Based Inference Through Robust Optimal Transport
Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya +1
When a statistical model lacks analytically tractable likelihoods, parametric statistical inference based on data generated from an unknown underlying distributi…
On Quantification of Borrowing of Information in Hierarchical Bayesian Models
Prasenjit Ghosh, Anirban Bhattacharya, Debdeep Pati
In this work, we offer a thorough analytical investigation into the role of shared hyperparameters in a hierarchical Bayesian model, examining their impact on information borrowing…
Robust Bayesian Inference on Riemannian Submanifold
Rong Tang, Anirban Bhattacharya, Debdeep Pati +1
Manifold-valued parameters routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian a…
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…
Factorized Fusion Shrinkage for Dynamic Relational Data
Peng Zhao, Anirban Bhattacharya, Debdeep Pati +1
Modern data science applications often involve complex relational data with dynamic structures. An abrupt change in such dynamic relational data is typically observed in systems th…
Coupling-based convergence assessment of some Gibbs samplers for high-dimensional Bayesian regression with shrinkage priors
Niloy Biswas, Anirban Bhattacharya, Pierre E. Jacob +1
We consider Markov chain Monte Carlo (MCMC) algorithms for Bayesian high-dimensional regression with continuous shrinkage priors. A common challenge with these algorithms is the ch…
Nonparametric Bayesian Deconvolution of a Symmetric Unimodal Density
Ya Su, Anirban Bhattacharya, Yan Zhang +2
We consider nonparametric measurement error density deconvolution subject to heteroscedastic measurement errors as well as symmetry about zero and shape constraints, in particular…
Efficient Bayesian shape-restricted function estimation with constrained Gaussian process priors
Pallavi Ray, Debdeep Pati, Anirban Bhattacharya
This article revisits the problem of Bayesian shape-restricted inference in the light of a recently developed approximate Gaussian process that admits an equivalent formulation of…
A Modified Sequential Probability Ratio Test
Sandipan Pramanik, Valen E. Johnson, Anirban Bhattacharya
We describe a modified sequential probability ratio test that can be used to reduce the average sample size required to perform statistical hypothesis tests at specified levels of…
Signal Adaptive Variable Selector for the Horseshoe Prior
Pallavi Ray, Anirban Bhattacharya
In this article, we propose a simple method to perform variable selection as a post model-fitting exercise using continuous shrinkage priors such as the popular horseshoe prior. Th…
Compressed Covariance Estimation With Automated Dimension Learning
Gautam Sabnis, Debdeep Pati, Anirban Bhattacharya
We propose a method for estimating a covariance matrix that can be represented as a sum of a low-rank matrix and a diagonal matrix. The proposed method compresses high-dimensional…
Empirical Bayes, SURE and Sparse Normal Mean Models
Xianyang Zhang, Anirban Bhattacharya
This paper studies the sparse normal mean models under the empirical Bayes framework. We focus on the mixture priors with an atom at zero and a density component centered at a data…
Bayesian sparse multiple regression for simultaneous rank reduction and variable selection
Antik Chakraborty, Anirban Bhattacharya, Bani K. Mallick
We develop a Bayesian methodology aimed at simultaneously estimating low-rank and row-sparse matrices in a high-dimensional multiple-response linear regression model. We consider a…
Partitioned Cross-Validation for Divide-and-Conquer Density Estimation
Anirban Bhattacharya, Jeffrey D. Hart
We present an efficient method to estimate cross-validation bandwidth parameters for kernel density estimation in very large datasets where ordinary cross-validation is rendered hi…
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…
Optimal Gaussian approximations to the posterior for log-linear models with Diaconis-Ylvisaker priors
James E. Johndrow, Anirban Bhattacharya
In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solu…
Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-Dimensional Settings
Minsuk Shin, Anirban Bhattacharya, Valen E. Johnson
Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures…
Bayesian factorizations of big sparse tensors
Jing Zhou, Anirban Bhattacharya, Amy Herring +1
It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited co…