Efficient sampling for Gaussian linear regression with arbitrary priors
This is an incremental improvement for researchers and practitioners in Bayesian statistics, enabling more efficient exploration of new shrinkage priors without custom algorithm development.
The paper tackles the problem of sampling in Bayesian linear regression with arbitrary priors by developing a slice sampler that is faster for large numbers of regressors and works with any prior with an evaluable density, resulting in better effective sample size per second compared to existing methods.
This paper 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.