{"id":"https://openalex.org/W2103343044","doi":"https://doi.org/10.1145/1190095.1190150","title":"On the efficiency of adaptive MCMC algorithms","display_name":"On the efficiency of adaptive MCMC algorithms","publication_year":2006,"publication_date":"2006-01-01","ids":{"openalex":"https://openalex.org/W2103343044","doi":"https://doi.org/10.1145/1190095.1190150","mag":"2103343044"},"language":"en","primary_location":{"id":"doi:10.1145/1190095.1190150","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1190095.1190150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st international conference on Performance evaluation methodolgies and tools  - valuetools '06","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110930504","display_name":"Christophe Andrieu","orcid":"https://orcid.org/0000-0001-9320-6398"},"institutions":[{"id":"https://openalex.org/I36234482","display_name":"University of Bristol","ror":"https://ror.org/0524sp257","country_code":"GB","type":"education","lineage":["https://openalex.org/I36234482"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Christophe Andrieu","raw_affiliation_strings":["University of Bristol, Bristol, UK","[university of bristol, Bristol, UK]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Bristol, Bristol, UK","institution_ids":["https://openalex.org/I36234482"]},{"raw_affiliation_string":"[university of bristol, Bristol, UK]","institution_ids":["https://openalex.org/I36234482"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070206514","display_name":"Yves F. Atchad\u00e9","orcid":"https://orcid.org/0000-0002-0487-6136"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Y. F. Atchad\u00e9","raw_affiliation_strings":["University of Ottawa, Ottawa, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Ottawa, Ottawa, Canada","institution_ids":["https://openalex.org/I153718931"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6101,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.72531847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"43","last_page":"43"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11152","display_name":"Stochastic processes and statistical mechanics","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.9160871505737305},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6259928941726685},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5855043530464172},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5131455063819885},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5129839777946472},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.49929070472717285},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48731908202171326},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.4636033773422241},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.41337263584136963},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26029032468795776},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2515478730201721},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.1276628077030182},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06236198544502258}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.9160871505737305},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6259928941726685},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5855043530464172},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5131455063819885},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5129839777946472},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.49929070472717285},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48731908202171326},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.4636033773422241},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.41337263584136963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26029032468795776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2515478730201721},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.1276628077030182},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06236198544502258},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1190095.1190150","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1190095.1190150","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st international conference on Performance evaluation methodolgies and tools  - valuetools '06","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W314780631","https://openalex.org/W1499021337","https://openalex.org/W1521318380","https://openalex.org/W1552672157","https://openalex.org/W1568229137","https://openalex.org/W1979641008","https://openalex.org/W1995780830","https://openalex.org/W2064871928","https://openalex.org/W2080577297","https://openalex.org/W2106606323","https://openalex.org/W2122337269","https://openalex.org/W2130416410","https://openalex.org/W2133555934","https://openalex.org/W3015241628","https://openalex.org/W3125965415"],"related_works":["https://openalex.org/W2151689585","https://openalex.org/W2380816257","https://openalex.org/W3087071515","https://openalex.org/W4283726152","https://openalex.org/W3093571331","https://openalex.org/W1525770572","https://openalex.org/W1485888979","https://openalex.org/W3172507773","https://openalex.org/W2806680938","https://openalex.org/W4302285290"],"abstract_inverted_index":{"We":[0,25,70],"study":[1],"a":[2,22,73],"class":[3,75],"of":[4,46,68,76],"adaptive":[5,32,47,65,77,82],"Markov":[6],"Chain":[7],"Monte":[8],"Carlo":[9],"(MCMC)":[10],"processes":[11],"which":[12,84],"aim":[13],"at":[14],"behaving":[15],"as":[16,91],"an":[17],"\"optimal\"":[18,35],"target":[19],"process":[20,33],"via":[21],"learning":[23],"procedure.":[24],"show,":[26],"under":[27],"appropriate":[28],"conditions,":[29],"that":[30],"the":[31,64],"and":[34,58,88],"(nonadaptive)":[36],"MCMC":[37,48,78,83],"algorithm":[38,67],"share":[39],"identical":[40],"asymptotic":[41],"properties.":[42],"The":[43],"special":[44],"case":[45],"algorithms":[49],"governed":[50],"by":[51],"stochastic":[52],"approximation":[53],"is":[54],"considered":[55],"in":[56],"details":[57],"we":[59],"apply":[60],"our":[61],"results":[62],"to":[63],"Metropolis":[66],"[1].":[69],"also":[71],"propose":[72],"new":[74],"algorithms,":[79],"called":[80],"quasi-perfect":[81],"possesses":[85],"appealing":[86],"theoretical":[87],"practical":[89],"properties,":[90],"demonstrated":[92],"through":[93],"numerical":[94],"simulations.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
