Taylor & Francis Group
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Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

posted on 2018-09-07, 21:51 authored by P. Richard Hahn, Jingyu He, Hedibert F. Lopes

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.


The authors gratefully acknowledge the Booth School of Business for support. The third author acknowledges the support of research fellowships from the CNPq and FAFESP, Brazil.