{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T09:30:58Z","timestamp":1725010258333},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[1999,9,1]],"date-time":"1999-09-01T00:00:00Z","timestamp":936144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1999,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Convergence rates, statistical efficiency and sampling costs are studied for the original and extended Swendsen\u2013Wang methods of generating a sample path {S \u00a0j, j\u22651} with equilibrium distribution \u03c0, with r distinct elements, on a finite state space X of size N \u00a01. Given S \u00a0j-1, each method uses auxiliary random variables to identify the subset of X from which S \u00a0j is to be randomly sampled. Let \u03c0min and \u03c0max denote respectively the smallest and largest elements in \u03c0 and let Nr denote the number of elements in \u03c0 with value \u03c0max. For a single auxiliary variable, uniform sampling from the subset and (N \u00a01\u2212Nr)\u03c0min+Nr\u03c0max\u22481, our results show rapid convergence and high statistical efficiency for large \u03c0min\/\u03c0max \u00a0or \u00a0Nr\/N \u00a01 and slow convergence and poor statistical efficiency for small \u03c0min\/\u03c0max \u00a0and \u00a0Nr\/N1. Other examples provide additional insight. For extended Swendsen\u2013Wang methods with non-uniform subset sampling, the analysis identifies the properties of a decomposition of \u03c0(x) that favour fast convergence and high statistical efficiency. In the absence of exploitable special structure, subset sampling can be costly regardless of which of these methods is employed.<\/jats:p>","DOI":"10.1111\/1467-9868.00197","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T19:35:20Z","timestamp":1047497720000},"page":"623-641","source":"Crossref","is-referenced-by-count":5,"title":["An Analysis of Swendsen\u2013Wang and Related Sampling Methods"],"prefix":"10.1093","volume":"61","author":[{"given":"George S.","family":"Fishman","sequence":"first","affiliation":[{"name":"University of North Carolina , Chapel Hill , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2002,1,6]]},"container-title":["Journal of the Royal Statistical Society Series B: Statistical Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2F1467-9868.00197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/61\/3\/623\/49589621\/jrsssb_61_3_623.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/61\/3\/623\/49589621\/jrsssb_61_3_623.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:57:22Z","timestamp":1679468242000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jrsssb\/article\/61\/3\/623\/7083212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1999,9,1]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2002,1,6]]},"published-print":{"date-parts":[[1999,9,1]]}},"URL":"https:\/\/doi.org\/10.1111\/1467-9868.00197","relation":{},"ISSN":["1369-7412","1467-9868"],"issn-type":[{"value":"1369-7412","type":"print"},{"value":"1467-9868","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[1999,9]]},"published":{"date-parts":[[1999,9,1]]}}}