Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection

• 成果类型: Article
• 署名作者: Song, Qifan; Sun, Yan; Ye, Mao; Liang, Faming
• 署名单位: Purdue University System; Purdue University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa029
• 发表日期: 2020
• 页码: 9971004
• 关键词: model

摘要

Stochastic gradient Markov chain Monte Carlo algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters. This paper proposes an extended stochastic gradient Markov chain Monte Carlo algorithm which, by introducing appropriate latent variables, can be applied to more general large-scale Bayesian computing problems, such as those involving dimension jumping and missing data. Numerical studies show that the proposed algorithm is highly scalable and much more efficient than traditional Markov chain Monte Carlo algorithms.