68 citations · 319 across the 71 of their papers we have counts for
30 papers · 1 filter
Bayesian Estimation of Continuous Brain Connectivity with Log-Gaussian Cox Processes
Jaehoan Kim, William Consagra, Debdeep Pati +1
Continuous structural connectivity describes the connection between any two points of the cortical surface by an intensity function. Based on the endpoints of tractography streamli…
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…
Stationary Point Constrained Inference via Diffeomorphisms
Michael Price, Debdeep Pati, Ning Ning
Stationary points or derivative zero crossings of a regression function correspond to points where a trend reverses, making their estimation scientifically important. Existing appr…
Global-Local Dirichlet Processes for Identifying Pan-Cancer Subpopulations Using Both Shared and Cancer-Specific Data
Arhit Chakrabarti, Yang Ni, Debdeep Pati +1
We consider the problem of clustering grouped data for which the observations may include group-specific variables in addition to the variables that are shared across groups. This…
An Interpretable Single-Index Mixed-Effects Model for Non-Gaussian National Survey Data
Qingyang Liu, Debdeep Pati, Dipankar Bandyopadhyay
This manuscript presents an innovative statistical model to quantify periodontal disease in the context of complex medical data. A mixed-effects model incorporating skewed random e…
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…
Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests
Somjit Roy, Pritam Dey, Bani K. Mallick +1
Symbolic regression has emerged as a powerful tool for artificial intelligence-driven scientific discovery by learning interpretable analytical expressions that reveal governing re…
A monotone single index model for spatially referenced multistate current status data
Snigdha Das, Minwoo Chae, Debdeep Pati +1
Assessment of multistate disease progression is commonplace in biomedical research, such as, in periodontal disease (PD). However, the presence of multistate current status endpoin…
Model-based inference in complex surveys through targeted resampling of weights
Snigdha Das, Dipankar Bandyopadhyay, Debdeep Pati
Survey data often arises from complex sampling designs, such as stratified or multistage sampling, with unequal inclusion probabilities. When sampling is informative, traditional i…
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods
Somjit Roy, Pritam Dey, Debdeep Pati +1
Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevert…
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…
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…
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…
Bayesian Copula Density Deconvolution for Zero-Inflated Data in Nutritional Epidemiology
Abhra Sarkar, Debdeep Pati, Bani K. Mallick +1
Estimating the marginal and joint densities of the long-term average intakes of different dietary components is an important problem in nutritional epidemiology. Since these variab…
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…
Shape-Constrained Univariate Density Estimation
Sutanoy Dasgupta, Debdeep Pati, Ian H. Jermyn +1
While the problem of estimating a probability density function (pdf) from its observations is classical, the estimation under additional shape constraints is both important and cha…
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…
A Two-Step Geometric Framework For Density Modeling
Sutanoy Dasgupta, Debdeep Pati, Anuj Srivastava
We introduce a novel two-step approach for estimating a probability density function (pdf) given its samples, with the second and important step coming from a geometric formulation…
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…