Debdeep Pati
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An Interpretable Single-Index Mixed-Effects Model for Non-Gaussian National Survey Data Open
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 effects and heavy-tailed residuals is introduced, ensuring r…
Hierarchical Bayesian Operator-induced Symbolic Regression Trees for Structural Learning of Scientific Expressions Open
The advent of Scientific Machine Learning has heralded a transformative era in scientific discovery, driving progress across diverse domains. Central to this progress is uncovering scientific laws from experimental data through symbolic re…
On Quantification of Borrowing of Information in Hierarchical Bayesian Models Open
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 and posterior inference. Our approach is rooted in a non-a…
View article: Global-local Dirichlet processes for identifying pan-cancer subpopulations using both shared and cancer-specific data
Global-local Dirichlet processes for identifying pan-cancer subpopulations using both shared and cancer-specific data Open
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 type of data is common in cancer genomics where the molecul…
A monotone single index model for spatially referenced multistate current status data Open
Assessment of multistate disease progression is commonplace in biomedical research, such as in periodontal disease (PD). However, the presence of multistate current status endpoints, where only a single snapshot of each subject’s progressi…
View article: Correction
Correction Open
Bhattacharya et al. (2015, Journal of the American Statistical Association 110(512): 1479-1490) introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chain Monte Carlo (MCMC) method to simulate posterior draws und…
Adaptive finite element type decomposition of Gaussian processes Open
In this paper, we investigate a class of approximate Gaussian processes (GP) obtained by taking a linear combination of compactly supported basis functions with the basis coefficients endowed with a dependent Gaussian prior distribution. T…
Scalable Efficient Inference in Complex Surveys through Targeted Resampling of Weights Open
Survey data often arises from complex sampling designs, such as stratified or multistage sampling, with unequal inclusion probabilities. When sampling is informative, traditional inference methods yield biased estimators and poor coverage.…
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods Open
Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevertheless, existing approaches often depend on either rigid mo…
VsusP: Variable Selection using Shrinkage Priors Open
Variable Selection using Shrinkage Priors Version 1.0.0Description Bayesian variable selection using shrinkage priors to identify significant variables in highdimensional datasets.The package includes methods for determining the number of …
EPSOM-Hyb: A General Purpose Estimator of Log-Marginal Likelihoods with Applications in Probabilistic Graphical Models Open
We consider the estimation of the marginal likelihood in Bayesian statistics, with primary emphasis on Gaussian graphical models, where the intractability of the marginal likelihood in high dimensions is a frequently researched problem. We…
View article: Algorithm 1045: A Covariate-Dependent Approach to Gaussian Graphical Modeling in R
Algorithm 1045: A Covariate-Dependent Approach to Gaussian Graphical Modeling in R Open
Graphical models are used to capture complex multivariate relationships and have applications in diverse disciplines such as biology, physics, and economics. Within this field, Gaussian graphical models aim to identify the pairs of variabl…
Constrained Reweighting of Distributions: An Optimal Transport Approach Open
We commonly encounter the problem of identifying an optimally weight-adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the…
GoodFitSBM: Monte Carlo Goodness-of-Fit Tests for Stochastic Block Models Open
Monte Carlo Goodness-of-Fit Tests for Stochastic Block Models Version 0.0.1 Description Performing goodness-of-fit tests for stochastic block models used to fit network data.Among the three variants discussed in Karwa et al. (2023) <doi:10.
A Gibbs Posterior Framework for Fair Clustering Open
The rise of machine learning-driven decision-making has sparked a growing emphasis on algorithmic fairness. Within the realm of clustering, the notion of balance is utilized as a criterion for attaining fairness, which characterizes a clus…
View article: Tail-adaptive Bayesian shrinkage
Tail-adaptive Bayesian shrinkage Open
Modern genomic studies are increasingly focused on discovering more and more interesting genes associated with a health response. Traditional shrinkage priors are primarily designed to detect a handful of signals from tens and thousands of…
Sparse additive Gaussian process with soft interactions Open
Additive nonparametric regression models provide an attractive tool for variable selection in high dimensions when the relationship between the response and predictors is complex. They offer greater flexibility compared to parametric non-l…
Robust Bayesian Inference on Riemannian Submanifold Open
Non-Euclidean spaces routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian approaches are applicable to such settings by considering an ambie…
Constrained Reweighting of Distributions: an Optimal Transport Approach Open
We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weights. Such constraints often manifest as restrictions on the…
Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels Open
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 blockmodel variants are log-linear in form when block assignm…
View article: Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors Open
Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $α$-TS, where we use a fractional or $α$-posterior ($α\in(0,1)$) instead of the s…
Memory Efficient And Minimax Distribution Estimation Under Wasserstein Distance Using Bayesian Histograms Open
We study Bayesian histograms for distribution estimation on $[0,1]^d$ under the Wasserstein $W_v, 1 \leq v < \infty$ distance in the i.i.d sampling regime. We newly show that when $d < 2v$, histograms possess a special \textit{memory effic…
View article: Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data Open
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets u…
On the Convergence of Coordinate Ascent Variational Inference Open
As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable …
Fair Clustering via Hierarchical Fair-Dirichlet Process Open
The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised machine learning approaches, there has naturally been a prolif…
View article: Blocked Gibbs sampler for hierarchical Dirichlet processes
Blocked Gibbs sampler for hierarchical Dirichlet processes Open
Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively focuses on the Chinese restaurant franchise…
Robust probabilistic inference via a constrained transport metric Open
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 a novel alternative by constructing an exponentially tilted…
View article: An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling Open
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-likelihood approach for graphical modeling which allows diffe…