{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T11:07:09Z","timestamp":1790766429783,"version":"4.1.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"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\/501100015023","name":"Labex Ecodec","doi-asserted-by":"publisher","award":["Ecodec\/ANR-11-LABX-0047"],"award-info":[{"award-number":["Ecodec\/ANR-11-LABX-0047"]}],"id":[{"id":"10.13039\/501100015023","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A standard way to move particles in a sequential Monte Carlo (SMC) sampler is to apply several steps of a Markov chain Monte Carlo (MCMC) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that waste-free SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decreases across iterations (as in tempering or rare event problems).<\/jats:p>","DOI":"10.1111\/rssb.12475","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T08:15:34Z","timestamp":1637050534000},"page":"114-148","source":"Crossref","is-referenced-by-count":24,"title":["Waste-Free Sequential Monte Carlo"],"prefix":"10.1093","volume":"84","author":[{"given":"Hai-Dang","family":"Dau","sequence":"first","affiliation":[{"name":"CREST-ENSAE and Institut Polytechnique de Paris , Palaiseau Cedex , France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Chopin","sequence":"additional","affiliation":[{"name":"CREST-ENSAE and Institut Polytechnique de Paris , Palaiseau Cedex , France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"2023022408361847300_","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1198\/016214505000001159","article-title":"Bayesian-optimal design via interacting particle systems","volume":"101","author":"Amzal","year":"2006","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408361847300_","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1080\/01621459.1997.10473661","article-title":"Dynamic conditional independence models and Markov chain Monte Carlo methods","volume":"92","author":"Berzuini","year":"1997","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408361847300_","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1214\/13-AAP951","article-title":"On the stability of sequential Monte Carlo methods in high 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