{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T17:58:23Z","timestamp":1789063103816,"version":"build-2803163510"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/K014463\/1"],"award-info":[{"award-number":["EP\/K014463\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/K034154\/1"],"award-info":[{"award-number":["EP\/K034154\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard schemes. When applied to Bayesian variable-selection problems, the novel algorithm is orders of magnitude more efficient than available alternative sampling schemes and enables fast and reliable fully Bayesian inferences with tens of thousand regressors.<\/jats:p>","DOI":"10.1111\/rssb.12316","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T22:05:58Z","timestamp":1553724358000},"page":"489-517","source":"Crossref","is-referenced-by-count":28,"title":["Scalable Importance Tempering and Bayesian Variable Selection"],"prefix":"10.1093","volume":"81","author":[{"given":"Giacomo","family":"Zanella","sequence":"first","affiliation":[{"name":"Bocconi University , Milan , Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gareth","family":"Roberts","sequence":"additional","affiliation":[{"name":"University of Warwick , Coventry , UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"2023022002403153400_","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1214\/08-AOS634","article-title":"On the computational complexity of MCMC-based estimators in large samples","volume":"37","author":"Belloni","year":"2009","journal-title":"Ann. 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