Name
Abstract | ||
|---|---|---|
In this note we derive a simple Bayesian sampler for linear regression with the horseshoe hierarchy. A new interpretation of the horseshoe model is presented, and extensions to logistic regression and alternative hierarchies, such as horseshoe+, are discussed. Due to the conjugacy of the proposed hierarchy, Chib’s algorithm may be used to easily compute the marginal likelihood of the model. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2015.2503725 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Bayesian regression,Markov chain Monte Carlo sampling,horseshoe estimator | Applied mathematics,Mathematical optimization,Bayesian linear regression,Marginal likelihood,Proper linear model,Conjugacy class,Bayesian multivariate linear regression,Statistics,Mathematics,Bayesian probability,Linear regression,Estimator | Journal |
Volume | Issue | ISSN |
23 | 1 | 1070-9908 |
Citations | PageRank | References |
6 | 1.01 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enes Makalic | 1 | 55 | 11.54 |
| Daniel F. Schmidt | 2 | 51 | 10.68 |