Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

Paul Hahn, Jingyu He, Hedibert F. Lopes

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

This article develops a slice sampler for Bayesian linear regression models with arbitrary priors. The new sampler has two advantages over current approaches. One, it is faster than many custom implementations that rely on auxiliary latent variables, if the number of regressors is large. Two, it can be used with any prior with a density function that can be evaluated up to a normalizing constant, making it ideal for investigating the properties of new shrinkage priors without having to develop custom sampling algorithms. The new sampler takes advantage of the special structure of the linear regression likelihood, allowing it to produce better effective sample size per second than common alternative approaches.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Bayesian computation
  • Linear regression
  • Shrinkage priors
  • Slice sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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