Dirichlet-Laplace priors for optimal shrinkage (vol 110, pg1479-1490, 2014 )
成果类型：
Correction; Early Access
署名作者：
Gruber, Luis; Kastner, Gregor; Bhattacharya, Anirban; Pati, Debdeep; Pillai, Natesh; Dunson, David
署名单位：
University of Klagenfurt; Texas A&M University System; Texas A&M University College Station; University of Wisconsin System; University of Wisconsin Madison; Harvard University; Duke University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2025.2540256
发表日期：
2025
关键词：
摘要：
Bhattacharya et al. introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chain Monte Carlo (MCMC) method to simulate posterior draws under this prior in a conditionally Gaussian setting. The original algorithm samples from conditional distributions in the wrong order, that is, it does not correctly sample from the joint posterior distribution of all latent variables. This note details the issue and provides two simple solutions: A correction to the original algorithm and a new algorithm based on an alternative, yet equivalent, formulation of the prior. This corrigendum does not affect the theoretical results in Bhattacharya et al.