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05/18/2023
Posterior Inference on Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
From the classical and influential works of Neal (1996), it is known tha...
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03/13/2023
Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation
Sparse structure learning in high-dimensional Gaussian graphical models ...
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12/06/2022
SURE-tuned Bridge Regression
Consider the ℓ_α regularized linear regression, also termed Bridge regre...
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10/14/2022
Individualized Inference in Bayesian Quantile Directed Acyclic Graphical Models
We propose an approach termed "qDAGx" for Bayesian covariate-dependent q...
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09/09/2022
A Laplace Mixture Representation of the Horseshoe and Some Implications
The horseshoe prior, defined as a half Cauchy scale mixture of normal, p...
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05/02/2022
Graphical Evidence
Marginal likelihood, also known as model evidence, is a fundamental quan...
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11/22/2021
Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics
Integrative analysis of multi-level pharmacogenomic data for modeling de...
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10/22/2021
Merging Two Cultures: Deep and Statistical Learning
Merging the two cultures of deep and statistical learning provides insig...
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04/21/2021
Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual
The problem of precision matrix estimation in a multivariate Gaussian mo...
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10/27/2020
Bayesian Variable Selection in Multivariate Nonlinear Regression with Graph Structures
Gaussian graphical models (GGMs) are well-established tools for probabil...
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11/14/2019
Kriging: Beyond Matérn
The Matérn covariance function is a popular choice for prediction in spa...
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04/24/2019
Horseshoe Regularization for Machine Learning in Complex and Deep Models
Since the advent of the horseshoe priors for regularization, global-loca...
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03/15/2019
Joint Mean-Covariance Estimation via the Horseshoe with an Application in Genomic Data Analysis
Seemingly unrelated regression is a natural framework for regressing mul...
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01/15/2018
Divide and Recombine for Large and Complex Data: Model Likelihood Functions using MCMC
In Divide & Recombine (D&R), big data are divided into subsets, each ana...
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02/23/2017
