{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T06:16:06Z","timestamp":1687760166062},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","funder":[{"name":"National Science Foundation","award":["NSF-DMS-1522398"],"award-info":[{"award-number":["NSF-DMS-1522398"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>An algorithm is proposed for computing\nequilibrium averages of Markov chains which suffer from metastability \u2013\nthe tendency to remain in one or more subsets of state space for\nlong time intervals. The algorithm, called the parallel replica method (or\nParRep), uses many parallel\nprocessors to explore these subsets more efficiently.\nNumerical simulations on a simple model demonstrate consistency of\nthe method. A proof of consistency is given in an idealized setting.\nThe parallel replica method can be considered a generalization of A. F. Voter's\nparallel replica dynamics, originally developed to efficiently\nsimulate metastable Langevin stochastic dynamics.<\/jats:p>","DOI":"10.1515\/mcma-2015-0110","type":"journal-article","created":{"date-parts":[[2015,11,4]],"date-time":"2015-11-04T17:01:17Z","timestamp":1446656477000},"page":"255-273","source":"Crossref","is-referenced-by-count":4,"title":["The parallel replica method for computing equilibrium averages of Markov chains"],"prefix":"10.1515","volume":"21","author":[{"given":"David","family":"Aristoff","sequence":"first","affiliation":[{"name":"841 Oval Drive, Fort Collins, CO 80523, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2015,11,4]]},"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2015-0110\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2015-0110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T15:02:05Z","timestamp":1680361325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2015-0110\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,4]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2015,12,1]]},"published-print":{"date-parts":[[2015,12,1]]}},"alternative-id":["10.1515\/mcma-2015-0110"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2015-0110","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"value":"0929-9629","type":"print"},{"value":"1569-3961","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,11,4]]}}}