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08/07/2023
Nonparametric Bayes multiresolution testing for high-dimensional rare events
In a variety of application areas, there is interest in assessing eviden...
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06/04/2023
Bayesian nonparametric modeling of latent partitions via Stirling-gamma priors
Dirichlet process mixtures are particularly sensitive to the value of th...
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05/06/2023
Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis
Factor analysis provides a canonical framework for imposing lower-dimens...
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04/21/2023
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing ...
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04/20/2023
Ellipsoid fitting with the Cayley transform
We introduce an algorithm, Cayley transform ellipsoid fitting (CTEF), th...
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03/31/2023
Bayesian Clustering via Fusing of Localized Densities
Bayesian clustering typically relies on mixture models, with each compon...
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03/19/2023
Robustifying likelihoods by optimistically re-weighting data
Likelihood-based inferences have been remarkably successful in wide-span...
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11/27/2022
Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes
In geostatistical problems with massive sample size, Gaussian processes ...
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11/01/2022
Tree Representations of Brain Structural Connectivity via Persistent Homology
The brain structural connectome is generated by a collection of white ma...
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05/24/2022
Bayesian Functional Principal Components Analysis using Relaxed Mutually Orthogonal Processes
Functional Principal Component Analysis (FPCA) is a prominent tool to ch...
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05/12/2022
Outlier Detection for Multi-Network Data
It has become routine in neuroscience studies to measure brain networks ...
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01/25/2022
Spatial meshing for general Bayesian multivariate models
Quantifying spatial and/or temporal associations in multivariate geoloca...
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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...
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01/21/2022
Curved factor analysis with the Ellipsoid-Gaussian distribution
There is a need for new models for characterizing dependence in multivar...
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12/22/2021
Bag of DAGs: Flexible Scalable Modeling of Spatiotemporal Dependence
We propose a computationally efficient approach to construct a class of ...
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10/14/2021
Inferring Manifolds From Noisy Data Using Gaussian Processes
In analyzing complex datasets, it is often of interest to infer lower di...
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09/24/2021
Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data
Mixed Membership Models (MMMs) are a popular family of latent structure ...
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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...
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06/03/2021
Bayesian inference on high-dimensional multivariate binary data
It has become increasingly common to collect high-dimensional binary dat...
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03/18/2021
Generalized infinite factorization models
Factorization models express a statistical object of interest in terms o...
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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...
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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...
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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 ...
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01/25/2021
Identifying Interpretable Discrete Latent Structures from Discrete Data
High dimensional categorical data are routinely collected in biomedical ...
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01/10/2021
Grid-Parametrize-Split (GriPS) for Improved Scalable Inference in Spatial Big Data Analysis
Rapid advancements in spatial technologies including Geographic Informat...
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12/02/2020
Spatial Multivariate Trees for Big Data Bayesian Regression
High resolution geospatial data are challenging because standard geostat...
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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...
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10/14/2020
Diffusion Based Gaussian Processes on Restricted Domains
In nonparametric regression and spatial process modeling, it is common f...
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09/17/2020
Bayesian Matrix Completion for Hypothesis Testing
The United States Environmental Protection Agency (EPA) screens thousand...
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09/04/2020
Density estimation and modeling on symmetric spaces
In many applications, data and/or parameters are supported on non-Euclid...
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07/16/2020
Extended Stochastic Block Models
Stochastic block models (SBM) are widely used in network science due to ...
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06/11/2020
Robust Optimization and Inference on Manifolds
We propose a robust and scalable procedure for general optimization and ...
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06/09/2020
A generalized Bayes framework for probabilistic clustering
Loss-based clustering methods, such as k-means and its variants, are sta...
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06/04/2020
Bayesian clustering of high-dimensional data
In many applications, it is of interest to cluster subjects based on ver...
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04/26/2020
Classification Trees for Imbalanced and Sparse Data: Surface-to-Volume Regularization
Classification algorithms face difficulties when one or more classes hav...
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04/17/2020
Bayesian semiparametric long memory models for discretized event data
We introduce a new class of semiparametric latent variable models for lo...
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04/03/2020
Composite mixture of log-linear models for categorical data
Multivariate categorical data are routinely collected in many applicatio...
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03/31/2020
Distributed Bayesian clustering using finite mixture of mixtures
In many modern applications, there is interest in analyzing enormous dat...
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03/31/2020
Distributed Bayesian clustering
In many modern applications, there is interest in analyzing enormous dat...
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03/17/2020
Nearest Neighbor Dirichlet Process
There is a rich literature on Bayesian nonparametric methods for unknown...
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01/12/2020
Reproducible Bootstrap Aggregating
Heterogeneity between training and testing data degrades reproducibility...
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12/11/2019
Diffusion based Gaussian process regression via heat kernel reconstruction
We propose an algorithm for Gaussian Process regression on an unknown em...
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11/15/2019
Batch correction of high-dimensional data
Biomedical research often produces high-dimensional data confounded by b...
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11/07/2019
Auto-encoding graph-valued data with applications to brain connectomes
Our interest focuses on developing statistical methods for analysis of b...
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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...
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10/21/2019
Soft Tensor Regression
Statistical methods relating tensor predictors to scalar outcomes in a r...
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10/07/2019
Perturbed factor analysis: Improving generalizability across studies
Factor analysis is routinely used for dimensionality reduction. However,...
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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...
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07/12/2019
Estimating densities with nonlinear support using Fisher-Gaussian kernels
Current tools for multivariate density estimation struggle when the dens...
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07/10/2019
