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09/17/2023
Gibbs Sampling using Anti-correlation Gaussian Data Augmentation, with Applications to L1-ball-type Models
L1-ball-type priors are a recent generalization of the spike-and-slab pr...
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04/04/2022
Low Tree-Rank Bayesian Vector Autoregression Model
Vector autoregressions have been widely used for modeling and analysis o...
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02/01/2022
Spectral Clustering, Spanning Forest, and Bayesian Forest Process
Spectral clustering algorithms are very popular. Starting from a pairwis...
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08/10/2021
Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality
In statistical applications, it is common to encounter parameters suppor...
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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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08/23/2020
Quasi-Bernoulli Stick-breaking: Infinite Mixture with Cluster Consistency
In mixture modeling and clustering application, the number of components...
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06/02/2020
l_1-ball Prior: Uncertainty Quantification with Exact Zeros
Lasso and l_1-regularization play a dominating role in high dimensional ...
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02/22/2020
Bayesian Multi-scale Modeling of Factor Matrix without using Partition Tree
The multi-scale factor models are particularly appealing for analyzing m...
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10/06/2019
Spiked Laplacian Graphs: Bayesian Community Detection in Heterogeneous Networks
In the network data analysis, it is common to encounter a large populati...
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07/24/2019
Transport Monte Carlo
In Bayesian inference, transport map is a promising alternative to the c...
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06/27/2019
Tuning-Free Disentanglement via Projection
In representation learning and non-linear dimension reduction, there is ...
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03/21/2019
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
High dimensional data often contain multiple facets, and several cluster...
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10/19/2018
Bayesian Distance Clustering
Model-based clustering is widely-used in a variety of application areas....
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07/17/2018
Mixed-Stationary Gaussian Process for Flexible Non-Stationary Modeling of Spatial Outcomes
Gaussian processes (GPs) are commonplace in spatial statistics. Although...
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01/04/2018
Bayesian Constraint Relaxation
Prior information often takes the form of parameter constraints. Bayesia...
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02/10/2015