{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T21:49:15Z","timestamp":1680385755418},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"4","funder":[{"name":"Ibn-al-Banna Laboratory of Mathematics and Applications (LIBMA) at Cadi Ayyad University"},{"name":"Hassan II Academy of Sciences and Technology"}],"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>The standard Coupling From The Past (CFTP) algorithm is an interesting tool to sample from exact stationary distribution of a Markov chain. But it is very expensive in time consuming for large chains. There is a monotone version of CFTP, called MCFTP, that is less time consuming for monotone chains. In this work, we propose two techniques to get monotone chain allowing use of MCFTP: widening technique based on adding two fictitious states and clustering technique based on partitioning the state space in clusters. Usefulness and efficiency of our approaches are showed through a sample of Markov Chain Monte Carlo simulations.<\/jats:p>","DOI":"10.1515\/mcma-2015-0111","type":"journal-article","created":{"date-parts":[[2015,11,4]],"date-time":"2015-11-04T17:01:17Z","timestamp":1446656477000},"page":"301-312","source":"Crossref","is-referenced-by-count":1,"title":["Widening and clustering techniques allowing the use of monotone CFTP algorithm"],"prefix":"10.1515","volume":"21","author":[{"given":"Mohamed Yasser","family":"Bounnite","sequence":"first","affiliation":[{"name":"Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdelaziz","family":"Nasroallah","sequence":"additional","affiliation":[{"name":"Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco"}],"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-2015-0111\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2015-0111\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T15:02:12Z","timestamp":1680361332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2015-0111\/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-2015-0111"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2015-0111","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]]}}}