Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

DOI10.1080/10618600.2018.1482762OpenAlexW2808660425MaRDI QIDQ3391190

P. Richard Hahn, Hedibert Freitas Lopes, Jingyu He

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.05738

zbMATH Keywords

linear regressionBayesian computationslice samplingshrinkage priors

Mathematics Subject Classification ID

Statistics (62-XX)

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Uses Software

• R
• Monomvn

Cites Work

• Unnamed Item
• Mean field variational Bayes for continuous sparse signal shrinkage: pitfalls and remedies
• Slice sampling. (With discussions and rejoinder)
• Inference with normal-gamma prior distributions in regression problems
• BAYESIAN HYPER-LASSOS WITH NON-CONVEX PENALIZATION
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• Bayesian lasso regression
• Generalized double Pareto shrinkage
• The Bayesian Bridge
• Dirichlet–Laplace Priors for Optimal Shrinkage