Details

Autor(en) / Beteiligte

• Hahn, P. Richard
• He, Jingyu
• Lopes, Hedibert F.

Titel

Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

Ist Teil von

• Journal of computational and graphical statistics, 2019-01, Vol.28 (1), p.142-154

Ort / Verlag

Alexandria: Taylor & Francis

Erscheinungsjahr

2019

Quelle

Taylor & Francis

Beschreibungen/Notizen

• 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.