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09/15/2023
High-Dimensional Bernstein Von-Mises Theorems for Covariance and Precision Matrices
This paper aims to examine the characteristics of the posterior distribu...
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05/23/2023
High-dimensional Posterior Consistency in Multi-response Regression models with Non-informative Priors for Error Covariance Matrix
The Inverse-Wishart (IW) distribution is a standard and popular choice o...
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01/14/2022
A generalized likelihood based Bayesian approach for scalable joint regression and covariance selection in high dimensions
The paper addresses joint sparsity selection in the regression coefficie...
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12/20/2021
Convergence properties of data augmentation algorithms for high-dimensional robit regression
The logistic and probit link functions are the most common choices for r...
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09/18/2021
Asynchronous and Distributed Data Augmentation for Massive Data Settings
Data augmentation (DA) algorithms are widely used for Bayesian inference...
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01/02/2021
Geometric ergodicity of Gibbs samplers for the Horseshoe and its regularized variants
The Horseshoe is a widely used and popular continuous shrinkage prior fo...
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05/18/2020
B-CONCORD – A scalable Bayesian high-dimensional precision matrix estimation procedure
Sparse estimation of the precision matrix under high-dimensional scaling...
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11/03/2019
Estimating accuracy of the MCMC variance estimator: a central limit theorem for batch means estimators
The batch means estimator of the MCMC variance is a simple and effective...
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03/08/2019
Consistent Bayesian Sparsity Selection for High-dimensional Gaussian DAG Models with Multiplicative and Beta-mixture Priors
Estimation of the covariance matrix for high-dimensional multivariate da...
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02/14/2019
Estimation of Gaussian directed acyclic graphs using partial ordering information with an application to dairy cattle data
Estimating a directed acyclic graph (DAG) from observational data repres...
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02/10/2019
A Bayesian Approach to Joint Estimation of Multiple Graphical Models
The problem of joint estimation of multiple graphical models from high d...
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08/27/2018
A Hybrid Alternative to Gibbs Sampling for Bayesian Latent Variable Models
Gibbs sampling is a widely popular Markov chain Monte Carlo algorithm wh...
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04/16/2018
Trace class Markov chains for the Normal-Gamma Bayesian shrinkage model
High-dimensional data, where the number of variables exceeds or is compa...
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11/02/2017
Consistent estimation of the spectrum of trace class data augmentation algorithms
Markov chain Monte Carlo is widely used in a variety of scientific appli...
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07/20/2013
