David B. Dunson

research

∙ 05/06/2023

Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis

Factor analysis provides a canonical framework for imposing lower-dimens...

0 Noirrit Kiran Chandra, et al. ∙

research

∙ 04/21/2023

Machine Learning and the Future of Bayesian Computation

Bayesian models are a powerful tool for studying complex data, allowing ...

0 Steven Winter, et al. ∙

research

∙ 04/20/2023

Ellipsoid fitting with the Cayley transform

We introduce an algorithm, Cayley transform ellipsoid fitting (CTEF), th...

0 Omar Melikechi, et al. ∙

research

∙ 03/31/2023

Bayesian Clustering via Fusing of Localized Densities

Bayesian clustering typically relies on mixture models, with each compon...

0 Alexander Dombowsky, et al. ∙

research

∙ 03/19/2023

Robustifying likelihoods by optimistically re-weighting data

Likelihood-based inferences have been remarkably successful in wide-span...

0 Miheer Dewaskar, et al. ∙

research

∙ 11/27/2022

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes

In geostatistical problems with massive sample size, Gaussian processes ...

0 Yichen Zhu, et al. ∙

research

∙ 11/01/2022

Tree Representations of Brain Structural Connectivity via Persistent Homology

The brain structural connectome is generated by a collection of white ma...

0 Didong Li, et al. ∙

research

∙ 05/24/2022

Bayesian Functional Principal Components Analysis using Relaxed Mutually Orthogonal Processes

Functional Principal Component Analysis (FPCA) is a prominent tool to ch...

0 James Matuk, et al. ∙

research

∙ 05/12/2022

Outlier Detection for Multi-Network Data

It has become routine in neuroscience studies to measure brain networks ...

0 Pritam Dey, et al. ∙

research

∙ 01/25/2022

Spatial meshing for general Bayesian multivariate models

Quantifying spatial and/or temporal associations in multivariate geoloca...

0 Michele Peruzzi, et al. ∙

research

∙ 01/24/2022

Inferring taxonomic placement from DNA barcoding allowing discovery of new taxa

In ecology it has become common to apply DNA barcoding to biological sam...

0 Alessandro Zito, et al. ∙

research

∙ 01/21/2022

Curved factor analysis with the Ellipsoid-Gaussian distribution

There is a need for new models for characterizing dependence in multivar...

0 Hanyu Song, et al. ∙

research

∙ 12/22/2021

Bag of DAGs: Flexible Scalable Modeling of Spatiotemporal Dependence

We propose a computationally efficient approach to construct a class of ...

0 Bora Jin, et al. ∙

research

∙ 10/14/2021

Inferring Manifolds From Noisy Data Using Gaussian Processes

In analyzing complex datasets, it is often of interest to infer lower di...

9 David B. Dunson, et al. ∙

research

∙ 09/24/2021

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data

Mixed Membership Models (MMMs) are a popular family of latent structure ...

0 Yuqi Gu, et al. ∙

research

∙ 06/30/2021

Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph

In multivariate data analysis, it is often important to estimate a graph...

0 Leo L. Duan, et al. ∙

research

∙ 06/03/2021

Bayesian inference on high-dimensional multivariate binary data

It has become increasingly common to collect high-dimensional binary dat...

0 Antik Chakraborty, et al. ∙

research

∙ 03/18/2021

Generalized infinite factorization models

Factorization models express a statistical object of interest in terms o...

0 Lorenzo Schiavon, et al. ∙

research

∙ 03/09/2021

Covariate-informed latent interaction models: Addressing geographic taxonomic bias in predicting bird-plant interactions

Climate change and reductions in natural habitats necessitate that we be...

0 Georgia Papadogeorgou, et al. ∙

research

∙ 03/05/2021

PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits

Our understanding of the structure of the brain and its relationships wi...

0 Rongjie Liu, et al. ∙

research

∙ 02/01/2021

Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment

Risk assessment instruments are used across the criminal justice system ...

0 Kristian Lum, et al. ∙

research

∙ 01/25/2021

Identifying Interpretable Discrete Latent Structures from Discrete Data

High dimensional categorical data are routinely collected in biomedical ...

0 Yuqi Gu, et al. ∙

research

∙ 01/10/2021

Grid-Parametrize-Split (GriPS) for Improved Scalable Inference in Spatial Big Data Analysis

Rapid advancements in spatial technologies including Geographic Informat...

0 Michele Peruzzi, et al. ∙

research

∙ 12/02/2020

Spatial Multivariate Trees for Big Data Bayesian Regression

High resolution geospatial data are challenging because standard geostat...

0 Michele Peruzzi, et al. ∙

research

∙ 10/18/2020

Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds

We propose a general scheme for solving convex and non-convex optimizati...

22 Lizhen Lin, et al. ∙

research

∙ 10/14/2020

Diffusion Based Gaussian Processes on Restricted Domains

In nonparametric regression and spatial process modeling, it is common f...

9 David B. Dunson, et al. ∙

research

∙ 09/17/2020

Bayesian Matrix Completion for Hypothesis Testing

The United States Environmental Protection Agency (EPA) screens thousand...

0 Bora Jin, et al. ∙

research

∙ 09/04/2020

Density estimation and modeling on symmetric spaces

In many applications, data and/or parameters are supported on non-Euclid...

0 Didong Li, et al. ∙

research

∙ 07/16/2020

Extended Stochastic Block Models

Stochastic block models (SBM) are widely used in network science due to ...

0 Sirio Legramanti, et al. ∙

research

∙ 06/11/2020

Robust Optimization and Inference on Manifolds

We propose a robust and scalable procedure for general optimization and ...

0 Lizhen Lin, et al. ∙

research

∙ 06/09/2020

A generalized Bayes framework for probabilistic clustering

Loss-based clustering methods, such as k-means and its variants, are sta...

11 Tommaso Rigon, et al. ∙

research

∙ 06/04/2020

Bayesian clustering of high-dimensional data

In many applications, it is of interest to cluster subjects based on ver...

0 Noirrit Kiran Chandra, et al. ∙

research

∙ 04/26/2020

Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization

Classification algorithms face difficulties when one or more classes hav...

60 Yichen Zhu, et al. ∙

research

∙ 04/17/2020

Bayesian semiparametric long memory models for discretized event data

We introduce a new class of semiparametric latent variable models for lo...

0 Antik Chakraborty, et al. ∙

research

∙ 04/03/2020

Composite mixture of log-linear models for categorical data

Multivariate categorical data are routinely collected in many applicatio...

0 Emanuele Aliverti, et al. ∙

research

∙ 03/31/2020

Distributed Bayesian clustering using finite mixture of mixtures

In many modern applications, there is interest in analyzing enormous dat...

0 Hanyu Song, et al. ∙

research

∙ 03/31/2020

Distributed Bayesian clustering

In many modern applications, there is interest in analyzing enormous dat...

0 Hanyu Song, et al. ∙

research

∙ 03/17/2020

Nearest Neighbor Dirichlet Process

There is a rich literature on Bayesian nonparametric methods for unknown...

14 Shounak Chattopadhyay, et al. ∙

research

∙ 01/12/2020

Reproducible Bootstrap Aggregating

Heterogeneity between training and testing data degrades reproducibility...

8 Meimei Liu, et al. ∙

research

∙ 12/11/2019

Diffusion based Gaussian process regression via heat kernel reconstruction

We propose an algorithm for Gaussian Process regression on an unknown em...

0 David B. Dunson, et al. ∙

research

∙ 11/15/2019

Batch correction of high-dimensional data

Biomedical research often produces high-dimensional data confounded by b...

0 Emanuele Aliverti, et al. ∙

research

∙ 11/07/2019

Auto-encoding graph-valued data with applications to brain connectomes

Our interest focuses on developing statistical methods for analysis of b...

23 Meimei Liu, et al. ∙

research

∙ 11/05/2019

Identifying main effects and interactions among exposures using Gaussian processes

This article is motivated by the problem of studying the joint effect of...

0 Federico Ferrari, et al. ∙

research

∙ 10/21/2019

Soft Tensor Regression

Statistical methods relating tensor predictors to scalar outcomes in a r...

0 Georgia Papadogeorgou, et al. ∙

research

∙ 10/07/2019

Perturbed factor analysis: Improving generalizability across studies

Factor analysis is routinely used for dimensionality reduction. However,...

0 Arkaprava Roy, et al. ∙

research

∙ 09/15/2019

Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation

Posterior computation for high-dimensional data with many parameters can...

0 Willem van den Boom, et al. ∙

research

∙ 07/12/2019

Estimating densities with nonlinear support using Fisher-Gaussian kernels

Current tools for multivariate density estimation struggle when the dens...

0 Minerva Mukhopadhyay, et al. ∙

research

∙ 07/10/2019

Bayesian inferences on uncertain ranks and orderings

It is common to be interested in rankings or order relationships among e...

0 Andres F. Barrientos, et al. ∙

research

∙ 06/29/2019

Geodesic Distance Estimation with Spherelets

Many statistical and machine learning approaches rely on pairwise distan...

3 Didong Li, et al. ∙

research

∙ 06/18/2019

Monte Carlo simulation on the Stiefel manifold via polar expansion

Motivated by applications to Bayesian inference for statistical models w...

0 Michael Jauch, et al. ∙

David B. Dunson

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