{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:11:26Z","timestamp":1758705086466,"version":"3.44.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000181","name":"Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA2386-23-1-4100"],"award-info":[{"award-number":["FA2386-23-1-4100"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Given a smooth function $ f $, we develop a general approach to turn Monte Carlo samples with expectation $ m $ into an unbiased estimate of $ f(m) $. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $ f $ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy to set their tuning parameters (which depend on $ m $) automatically, with a view to making the whole approach simple to use. We develop our methods for the specific functions $ f(x)=\\log x $ and $ f(x)=1\/x $, as they arise in several statistical applications such as maximum likelihood estimation of latent variable models and Bayesian inference for unnormalized models. Detailed numerical studies are performed for a range of applications to determine how competitive and reliable the proposed approach is.<\/jats:p>","DOI":"10.1093\/biomet\/asaf030","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T07:36:52Z","timestamp":1744270612000},"source":"Crossref","is-referenced-by-count":0,"title":["Towards a turnkey approach for unbiased Monte Carlo estimation of smooth functions of expectations"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0628-5815","authenticated-orcid":false,"given":"Nicolas","family":"Chopin","sequence":"first","affiliation":[{"name":"ENSAE, Institut Polytechnique de Paris , 5 Avenue Le Chatelier , 91120 Palaiseau,","place":["France"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3887-7134","authenticated-orcid":false,"given":"Francesca R","family":"Crucinio","sequence":"additional","affiliation":[{"name":"ESOMAS, University of Torino , Via Verdi 8 , 10124 Torino,","place":["Italy"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumeetpal S","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Mathematics and Applied Statistics, University of Wollongong , Northfields Avenue , Wollongong, New South Wales 2522,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"2025092312570531300_asaf030-B1","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.3150\/16-BEJ911","article-title":"Unbiased Monte Carlo: posterior estimation for intractable\/infinite-dimensional models","volume":"24","author":"Agapiou","year":"2018","journal-title":"Bernoulli"},{"key":"2025092312570531300_asaf030-B2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1214\/11-AOAS499","article-title":"A stochastic algorithm for probabilistic independent component analysis","volume":"6","author":"Allassonni\u00e8re","year":"2012","journal-title":"Ann. 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