10.1080/10618600.2015.1012293Journal of Computational and Graphical Statistics252626-64
We devise a variational Bayes algorithm for fast approximate inference in Bayesian Generalized Extre...
With increasingly efficient data collection methods, scientists are interested in quickly analyzing ...
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency es...
<div><p>The article develops a hybrid Variational Bayes algorithm that combines the mean-field and s...
We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with cro...
Linear mixed models are a versatile statistical tool to study data by accounting for fixed effects a...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
We show how variational Bayesian inference can be implemented for very large generalized linear mode...
In this study, we propose a parallel programming method for linear mixed models (LMM) generated from...
In this work, we propose a novel approximated collapsed variational Bayes approach to model selectio...
Linear mixed models are a versatile statistical tool to study data by accounting for fixed effects a...
Bayesian methods have been widely used nowadays. This dissertation presents new research within the ...
10.1080/10618600.2017.1330205Journal of Computational and Graphical Statistics264873-88
In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is...
We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Gener...
We devise a variational Bayes algorithm for fast approximate inference in Bayesian Generalized Extre...
With increasingly efficient data collection methods, scientists are interested in quickly analyzing ...
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency es...
<div><p>The article develops a hybrid Variational Bayes algorithm that combines the mean-field and s...
We derive streamlined mean field variational Bayes algorithms for fitting linear mixed models with cro...
Linear mixed models are a versatile statistical tool to study data by accounting for fixed effects a...
Variational Inference (VI) has become a popular technique to approximate difficult-to-compute poster...
We show how variational Bayesian inference can be implemented for very large generalized linear mode...
In this study, we propose a parallel programming method for linear mixed models (LMM) generated from...
In this work, we propose a novel approximated collapsed variational Bayes approach to model selectio...
Linear mixed models are a versatile statistical tool to study data by accounting for fixed effects a...
Bayesian methods have been widely used nowadays. This dissertation presents new research within the ...
10.1080/10618600.2017.1330205Journal of Computational and Graphical Statistics264873-88
In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is...
We develop Mean Field Variational Bayes methodology for fast approximate inference in Bayesian Gener...
We devise a variational Bayes algorithm for fast approximate inference in Bayesian Generalized Extre...
With increasingly efficient data collection methods, scientists are interested in quickly analyzing ...
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency es...
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