{"id":"https://openalex.org/W2767963520","doi":"https://doi.org/10.1109/icdsp.2017.8096043","title":"Anti-tempered layered adaptive importance sampling","display_name":"Anti-tempered layered adaptive importance sampling","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2767963520","doi":"https://doi.org/10.1109/icdsp.2017.8096043","mag":"2767963520"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2017.8096043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"},"type":"preprint","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/A5016290792","display_name":"Luca Martino","orcid":"https://orcid.org/0000-0002-7611-6558"},"institutions":[{"id":"https://openalex.org/I16097986","display_name":"Universitat de Val\u00e8ncia","ror":"https://ror.org/043nxc105","country_code":"ES","type":"education","lineage":["https://openalex.org/I16097986"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Luca Martino","raw_affiliation_strings":["Image Processing Laboratory, Universitat de Valencia, Spain"],"affiliations":[{"raw_affiliation_string":"Image Processing Laboratory, Universitat de Valencia, Spain","institution_ids":["https://openalex.org/I16097986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085691944","display_name":"V\u0301\u0131ctor Elvira","orcid":"https://orcid.org/0000-0002-8967-4866"},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I4210133642","display_name":"IMT Nord Europe","ror":"https://ror.org/042rh9p26","country_code":"FR","type":"education","lineage":["https://openalex.org/I205703379","https://openalex.org/I4210133642"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Victor Elvira","raw_affiliation_strings":["IMT Lille Douai & CRIStAL (UMR CNRS 9189), Villeneuve d\u2019 Ascq, France","IMT Lille Douai & CRIStAL (UMR CNRS 9189), Villeneuve d' Ascq, France"],"affiliations":[{"raw_affiliation_string":"IMT Lille Douai & CRIStAL (UMR CNRS 9189), Villeneuve d\u2019 Ascq, France","institution_ids":["https://openalex.org/I4210133642","https://openalex.org/I1294671590"]},{"raw_affiliation_string":"IMT Lille Douai & CRIStAL (UMR CNRS 9189), Villeneuve d' Ascq, France","institution_ids":["https://openalex.org/I4210133642","https://openalex.org/I1294671590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084123747","display_name":"David Luengo","orcid":"https://orcid.org/0000-0001-7407-3630"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"David Luengo","raw_affiliation_strings":["Dep. of Signal Theory and Communic., Universidad Polit\u00e9cnica de Madrid, Madrid, Spain"],"affiliations":[{"raw_affiliation_string":"Dep. of Signal Theory and Communic., Universidad Polit\u00e9cnica de Madrid, Madrid, Spain","institution_ids":["https://openalex.org/I88060688"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5016290792"],"corresponding_institution_ids":["https://openalex.org/I16097986"],"apc_list":null,"apc_paid":null,"fwci":0.45835928,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.71488219,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983999729156494,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9962999820709229,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.9244481921195984},{"id":"https://openalex.org/keywords/parallel-tempering","display_name":"Parallel tempering","score":0.8212593793869019},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.651772677898407},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.5361409783363342},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5354123115539551},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5209530591964722},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5201423764228821},{"id":"https://openalex.org/keywords/rejection-sampling","display_name":"Rejection sampling","score":0.5063363313674927},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.5030221343040466},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4842786192893982},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48095574975013733},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4776638448238373},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4701412320137024},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.45287570357322693},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.43262630701065063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3800785541534424},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.3212256133556366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24168789386749268},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2135491669178009}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.9244481921195984},{"id":"https://openalex.org/C187653413","wikidata":"https://www.wikidata.org/wiki/Q7135015","display_name":"Parallel tempering","level":5,"score":0.8212593793869019},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.651772677898407},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.5361409783363342},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5354123115539551},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5209530591964722},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5201423764228821},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.5063363313674927},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.5030221343040466},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4842786192893982},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48095574975013733},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4776638448238373},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4701412320137024},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.45287570357322693},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.43262630701065063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3800785541534424},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3212256133556366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24168789386749268},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2135491669178009},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdsp.2017.8096043","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2017.8096043","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 22nd International Conference on Digital Signal Processing (DSP)","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":36,"referenced_works":["https://openalex.org/W280128266","https://openalex.org/W788570312","https://openalex.org/W1235362104","https://openalex.org/W1481030134","https://openalex.org/W1539591727","https://openalex.org/W1623013971","https://openalex.org/W1665662210","https://openalex.org/W1674392119","https://openalex.org/W1979969656","https://openalex.org/W1981920009","https://openalex.org/W1982942585","https://openalex.org/W2001848173","https://openalex.org/W2006722592","https://openalex.org/W2069690729","https://openalex.org/W2069739265","https://openalex.org/W2090346540","https://openalex.org/W2091040707","https://openalex.org/W2094509095","https://openalex.org/W2135973421","https://openalex.org/W2156840067","https://openalex.org/W2179435707","https://openalex.org/W2272328839","https://openalex.org/W2516145422","https://openalex.org/W2519528731","https://openalex.org/W2951522206","https://openalex.org/W3037960898","https://openalex.org/W3098203061","https://openalex.org/W3102506071","https://openalex.org/W3103126047","https://openalex.org/W3105327062","https://openalex.org/W3106038217","https://openalex.org/W3124477112","https://openalex.org/W4240844614","https://openalex.org/W4242067627","https://openalex.org/W4292691288","https://openalex.org/W6789266572"],"related_works":["https://openalex.org/W1483435625","https://openalex.org/W1593554773","https://openalex.org/W3097509027","https://openalex.org/W4295750535","https://openalex.org/W2539839227","https://openalex.org/W105676162","https://openalex.org/W4246060305","https://openalex.org/W2003732947","https://openalex.org/W2914481316","https://openalex.org/W1483665564"],"abstract_inverted_index":{"Monte":[0,39],"Carlo":[1,40],"(MC)":[2],"methods":[3],"are":[4],"widely":[5],"used":[6,58],"for":[7],"Bayesian":[8],"inference":[9],"in":[10,59,86],"signal":[11],"processing,":[12],"machine":[13],"learning":[14],"and":[15,36],"statistics.":[16],"In":[17],"this":[18],"work,":[19],"we":[20],"introduce":[21],"an":[22,60,80,91],"adaptive":[23],"importance":[24],"sampler":[25],"which":[26],"mixes":[27],"together":[28],"the":[29,32,48,52,71,87,95,98,106,109],"benefits":[30],"of":[31,51,70,97,108],"Importance":[33],"Sampling":[34],"(IS)":[35],"Markov":[37],"Chain":[38],"(MCMC)":[41],"approaches.":[42],"Different":[43],"parallel":[44],"MCMC":[45,64],"chains":[46],"provide":[47,79],"location":[49],"parameters":[50],"proposal":[53],"probability":[54],"density":[55],"functions":[56],"(pdfs)":[57],"IS":[61],"method.":[62],"The":[63],"algorithms":[65],"consider":[66],"a":[67],"tempered":[68],"version":[69],"posterior":[72],"distribution":[73],"as":[74],"invariant":[75],"density.":[76],"We":[77],"also":[78],"exhaustive":[81],"theoretical":[82],"support":[83],"explaining":[84],"why,":[85],"presented":[88],"technique,":[89],"even":[90],"anti-tempering":[92],"strategy":[93],"(reducing":[94],"scaling":[96],"posterior)":[99],"can":[100],"be":[101],"beneficial.":[102],"Numerical":[103],"results":[104],"confirm":[105],"advantages":[107],"proposed":[110],"scheme.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
