{"id":"https://openalex.org/W2020938661","doi":"https://doi.org/10.1162/neco.2010.01-09-943","title":"Bayesian Inference Based on Stationary Fokker-Planck Sampling","display_name":"Bayesian Inference Based on Stationary Fokker-Planck Sampling","publication_year":2010,"publication_date":"2010-02-08","ids":{"openalex":"https://openalex.org/W2020938661","doi":"https://doi.org/10.1162/neco.2010.01-09-943","mag":"2020938661","pmid":"https://pubmed.ncbi.nlm.nih.gov/20141472"},"language":"en","primary_location":{"id":"doi:10.1162/neco.2010.01-09-943","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco.2010.01-09-943","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5082080858","display_name":"Arturo Berrones","orcid":"https://orcid.org/0000-0002-9615-9088"},"institutions":[{"id":"https://openalex.org/I169046204","display_name":"Universidad Aut\u00f3noma de Nuevo Le\u00f3n","ror":"https://ror.org/01fh86n78","country_code":"MX","type":"education","lineage":["https://openalex.org/I169046204"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Arturo Berrones","raw_affiliation_strings":["Posgrado en Ingenier\u00eda de Sistemas, Centro de Innovaci\u00f3n, Investigaci\u00f3n y Desarrollo en Ingenier\u00eda y Tecnolog\u00eda, Facultad de Ingenier\u00eda Mec\u00e1nica y El\u00e9ctrica, Universidad Aut\u00f3noma de Nuevo Le\u00f3n, San Nicol\u00e1s de los Garza, NL 66450, M\u00e9xico"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Posgrado en Ingenier\u00eda de Sistemas, Centro de Innovaci\u00f3n, Investigaci\u00f3n y Desarrollo en Ingenier\u00eda y Tecnolog\u00eda, Facultad de Ingenier\u00eda Mec\u00e1nica y El\u00e9ctrica, Universidad Aut\u00f3noma de Nuevo Le\u00f3n, San Nicol\u00e1s de los Garza, NL 66450, M\u00e9xico","institution_ids":["https://openalex.org/I169046204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5082080858"],"corresponding_institution_ids":["https://openalex.org/I169046204"],"apc_list":null,"apc_paid":null,"fwci":0.9863,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.833621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"22","issue":"6","first_page":"1573","last_page":"1596"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.996999979019165,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.996999979019165,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9894000291824341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5842961668968201},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.583063542842865},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5077913403511047},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49741795659065247},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.48717233538627625},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4624811112880707},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.45672258734703064},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4393559396266937},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.43225809931755066},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4040239751338959},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3582991361618042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3536945581436157},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.3329281508922577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3320358097553253},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24748694896697998},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10412654280662537}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5842961668968201},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.583063542842865},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5077913403511047},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49741795659065247},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.48717233538627625},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4624811112880707},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.45672258734703064},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4393559396266937},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.43225809931755066},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4040239751338959},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3582991361618042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3536945581436157},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3329281508922577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3320358097553253},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24748694896697998},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10412654280662537},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009010","descriptor_name":"Monte Carlo Method","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009010","descriptor_name":"Monte Carlo Method","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009010","descriptor_name":"Monte Carlo Method","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015983","descriptor_name":"Selection Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015983","descriptor_name":"Selection Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015983","descriptor_name":"Selection Bias","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055641","descriptor_name":"Mathematical Concepts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055641","descriptor_name":"Mathematical Concepts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D055641","descriptor_name":"Mathematical Concepts","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1162/neco.2010.01-09-943","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco.2010.01-09-943","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:20141472","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/20141472","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.744.8275","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.744.8275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://arxiv.org/pdf/0901.1144.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321739","display_name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","ror":"https://ror.org/059ex5q34"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W188554010","https://openalex.org/W197998369","https://openalex.org/W1501639396","https://openalex.org/W1507695701","https://openalex.org/W1517729176","https://openalex.org/W1532044016","https://openalex.org/W1556278552","https://openalex.org/W1562847193","https://openalex.org/W1567512734","https://openalex.org/W1600310449","https://openalex.org/W1971758257","https://openalex.org/W1984167083","https://openalex.org/W1987694497","https://openalex.org/W2014927347","https://openalex.org/W2019486793","https://openalex.org/W2088721847","https://openalex.org/W2101222264","https://openalex.org/W2102787760","https://openalex.org/W2111051539","https://openalex.org/W2114849571","https://openalex.org/W2128266748","https://openalex.org/W2154317083","https://openalex.org/W2482043716","https://openalex.org/W2896916590","https://openalex.org/W3008437073","https://openalex.org/W3100951938","https://openalex.org/W3101991304","https://openalex.org/W3123239371","https://openalex.org/W4232995451","https://openalex.org/W4254259096","https://openalex.org/W4299429830"],"related_works":["https://openalex.org/W118730952","https://openalex.org/W3168765527","https://openalex.org/W27102384","https://openalex.org/W2146501959","https://openalex.org/W2617021092","https://openalex.org/W1607381525","https://openalex.org/W2791210712","https://openalex.org/W3121470121","https://openalex.org/W3126140132","https://openalex.org/W2075498446"],"abstract_inverted_index":{"A":[0,125],"novel":[1],"formalism":[2],"for":[3,42,55],"bayesian":[4,109],"learning":[5,95],"in":[6,97,120,134,196],"the":[7,20,23,31,38,49,56,61,71,86,91,98,116,135,159,171,205],"context":[8,99],"of":[9,22,60,69,100,127,138,161,165,179,193,198],"complex":[10],"inference":[11,110],"models":[12],"is":[13,17,83,143,147,151],"proposed.":[14],"The":[15,190],"method":[16,173],"based":[18],"on":[19],"use":[21],"stationary":[24],"Fokker-Planck":[25,35],"(SFP)":[26],"approach":[27],"to":[28,76,85,153],"sample":[29],"from":[30,115,140],"posterior":[32,62,117],"density.":[33],"Stationary":[34],"sampling":[36,80,139],"generalizes":[37],"Gibbs":[39,79],"sampler":[40],"algorithm":[41],"arbitrary":[43,141],"and":[44,58,107,111,122],"unknown":[45],"conditional":[46],"densities.":[47],"By":[48,90],"SFP":[50,128,150,172],"procedure,":[51],"approximate":[52,72],"analytical":[53,92],"expressions":[54],"conditionals":[57,73],"marginals":[59,93],"can":[63,183],"be":[64,184],"constructed.":[65],"At":[66],"each":[67],"stage":[68],"SFP,":[70,194],"are":[74,104,118],"used":[75],"define":[77],"a":[78,162,176],"process,":[81],"which":[82],"convergent":[84],"full":[87],"joint":[88],"posterior.":[89],"efficient":[94],"methods":[96],"artificial":[101],"neural":[102],"networks":[103],"outlined.":[105],"Offline":[106],"incremental":[108],"maximum":[112],"likelihood":[113],"estimation":[114],"performed":[119],"classification":[121],"regression":[123],"examples.":[124],"comparison":[126],"with":[129,204],"other":[130],"Monte":[131],"Carlo":[132],"strategies":[133],"general":[136],"problem":[137],"densities":[142],"also":[144],"presented.":[145],"It":[146],"shown":[148],"that":[149,182],"able":[152],"jump":[154],"large":[155],"low-probability":[156],"regions":[157],"without":[158],"need":[160],"careful":[163],"tuning":[164],"any":[166],"step-size":[167],"parameter.":[168],"In":[169],"fact,":[170],"requires":[174],"only":[175],"small":[177],"set":[178],"meaningful":[180],"parameters":[181],"selected":[185],"following":[186],"clear,":[187],"problem-independent":[188],"guidelines.":[189],"computation":[191],"cost":[192],"measured":[195],"terms":[197],"loss":[199],"function":[200],"evaluations,":[201],"grows":[202],"linearly":[203],"given":[206],"model's":[207],"dimension.":[208]},"counts_by_year":[{"year":2014,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
