TY - JOUR
T1 - Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors
AU - Hahn, P. Richard
AU - He, Jingyu
AU - Lopes, Hedibert F.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Bayesian computation
KW - Linear regression
KW - Shrinkage priors
KW - Slice sampling
KW - Bayesian computation
KW - Linear regression
KW - Shrinkage priors
KW - Slice sampling
KW - Bayesian computation
KW - Linear regression
KW - Shrinkage priors
KW - Slice sampling
UR - http://www.scopus.com/inward/record.url?scp=85053314959&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85053314959&origin=recordpage
UR - http://jingyuhe.com/files/bayeslm.pdf
U2 - 10.1080/10618600.2018.1482762
DO - 10.1080/10618600.2018.1482762
M3 - RGC 21 - Publication in refereed journal
VL - 28
SP - 142
EP - 154
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
SN - 1061-8600
IS - 1
ER -