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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.<\/jats:p>","DOI":"10.1093\/biomet\/asaa029","type":"journal-article","created":{"date-parts":[[2020,3,25]],"date-time":"2020-03-25T04:44:31Z","timestamp":1585111471000},"page":"997-1004","source":"Crossref","is-referenced-by-count":16,"title":["Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection"],"prefix":"10.1093","volume":"107","author":[{"given":"Qifan","family":"Song","sequence":"first","affiliation":[{"name":"Department of Statistics, Purdue University, 250 N. 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