{"id":"https://openalex.org/W3086712597","doi":"https://doi.org/10.1007/s11222-022-10083-5","title":"Augmented pseudo-marginal Metropolis\u2013Hastings for partially observed diffusion processes","display_name":"Augmented pseudo-marginal Metropolis\u2013Hastings for partially observed diffusion processes","publication_year":2022,"publication_date":"2022-02-15","ids":{"openalex":"https://openalex.org/W3086712597","doi":"https://doi.org/10.1007/s11222-022-10083-5","mag":"3086712597"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-022-10083-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10083-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10083-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10083-5.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021773740","display_name":"Andrew Golightly","orcid":"https://orcid.org/0000-0001-6730-1279"},"institutions":[{"id":"https://openalex.org/I84884186","display_name":"Newcastle University","ror":"https://ror.org/01kj2bm70","country_code":"GB","type":"education","lineage":["https://openalex.org/I84884186"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Andrew Golightly","raw_affiliation_strings":["School of Mathematics, Statistics and Physics, Newcastle University, Newcastle Upon Tyne, UK"],"affiliations":[{"raw_affiliation_string":"School of Mathematics, Statistics and Physics, Newcastle University, Newcastle Upon Tyne, UK","institution_ids":["https://openalex.org/I84884186"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052870306","display_name":"Chris Sherlock","orcid":"https://orcid.org/0000-0002-2429-3157"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chris Sherlock","raw_affiliation_strings":["Department of Mathematics and Statistics, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5021773740"],"corresponding_institution_ids":["https://openalex.org/I84884186"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":0.4928,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.62642045,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"32","issue":"1","first_page":null,"last_page":null},"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":0.9980000257492065,"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":0.9980000257492065,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9961000084877014,"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/T10928","display_name":"Probabilistic and Robust Engineering Design","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.7131649851799011},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6360628604888916},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.6031972765922546},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5927066802978516},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.5465235710144043},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5376689434051514},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.523430347442627},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43661078810691833},{"id":"https://openalex.org/keywords/marginal-distribution","display_name":"Marginal distribution","score":0.42246416211128235},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.41920697689056396},{"id":"https://openalex.org/keywords/marginal-likelihood","display_name":"Marginal likelihood","score":0.41423851251602173},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.41173186898231506},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3475433886051178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3388708531856537},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2994117736816406},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.21511781215667725},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.21059724688529968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15338024497032166},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.09796315431594849},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.08352512121200562}],"concepts":[{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.7131649851799011},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6360628604888916},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.6031972765922546},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5927066802978516},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.5465235710144043},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5376689434051514},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.523430347442627},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43661078810691833},{"id":"https://openalex.org/C165216359","wikidata":"https://www.wikidata.org/wiki/Q670653","display_name":"Marginal distribution","level":3,"score":0.42246416211128235},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.41920697689056396},{"id":"https://openalex.org/C95923904","wikidata":"https://www.wikidata.org/wiki/Q6760420","display_name":"Marginal likelihood","level":3,"score":0.41423851251602173},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.41173186898231506},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3475433886051178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3388708531856537},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2994117736816406},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.21511781215667725},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.21059724688529968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15338024497032166},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.09796315431594849},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08352512121200562},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1007/s11222-022-10083-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10083-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10083-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},{"id":"pmh:oai:dro.dur.ac.uk.OAI2:35639","is_oa":true,"landing_page_url":"http://dro.dur.ac.uk/35639/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196258","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"Statistics and Computing, 2022, Vol.32, pp.21 [Peer Reviewed Journal]","raw_type":"Article"},{"id":"pmh:oai:eprints.lancs.ac.uk:165561","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"PeerReviewed"},{"id":"pmh:oai:durham-repository.worktribe.com:1217379","is_oa":true,"landing_page_url":"https://durham-repository.worktribe.com/output/1217379","pdf_url":null,"source":{"id":"https://openalex.org/S4306400188","display_name":"Durham Research Online (Durham University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I190082696","host_organization_name":"Durham University","host_organization_lineage":["https://openalex.org/I190082696"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"publishedVersion"},{"id":"pmh:oai:eprints.ncl.ac.uk:279732","is_oa":true,"landing_page_url":"https://eprints.ncl.ac.uk/279732","pdf_url":null,"source":{"id":"https://openalex.org/S4306402485","display_name":"Newcastle University ePrints (Newcastle Univesity)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I84884186","host_organization_name":"Newcastle University","host_organization_lineage":["https://openalex.org/I84884186"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"doi:10.1007/s11222-022-10083-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-022-10083-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-022-10083-5.pdf","source":{"id":"https://openalex.org/S5437875","display_name":"Statistics and Computing","issn_l":"0960-3174","issn":["0960-3174","1573-1375"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistics and Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3086712597.pdf","grobid_xml":"https://content.openalex.org/works/W3086712597.grobid-xml"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W229871693","https://openalex.org/W277831955","https://openalex.org/W1481750590","https://openalex.org/W1499965417","https://openalex.org/W1501586228","https://openalex.org/W1531413759","https://openalex.org/W1549853756","https://openalex.org/W1946177469","https://openalex.org/W2003456014","https://openalex.org/W2010522529","https://openalex.org/W2016321914","https://openalex.org/W2023581626","https://openalex.org/W2031294093","https://openalex.org/W2053644777","https://openalex.org/W2059448777","https://openalex.org/W2088413985","https://openalex.org/W2090805023","https://openalex.org/W2091860746","https://openalex.org/W2101958901","https://openalex.org/W2111288282","https://openalex.org/W2116217424","https://openalex.org/W2122028584","https://openalex.org/W2123726866","https://openalex.org/W2125488105","https://openalex.org/W2132072575","https://openalex.org/W2134160730","https://openalex.org/W2134589175","https://openalex.org/W2147639966","https://openalex.org/W2150466720","https://openalex.org/W2153181693","https://openalex.org/W2168282770","https://openalex.org/W2196933485","https://openalex.org/W2315417698","https://openalex.org/W2322019262","https://openalex.org/W2401024973","https://openalex.org/W2436131410","https://openalex.org/W2486999229","https://openalex.org/W2563368355","https://openalex.org/W2761522159","https://openalex.org/W2962690136","https://openalex.org/W2964002469","https://openalex.org/W3004305852","https://openalex.org/W3019796668","https://openalex.org/W3036062512","https://openalex.org/W3099250650","https://openalex.org/W3099969635","https://openalex.org/W3100622648","https://openalex.org/W3103996751","https://openalex.org/W3104146538","https://openalex.org/W3106318495","https://openalex.org/W3121363706","https://openalex.org/W3123235823","https://openalex.org/W4205814206","https://openalex.org/W4233487859","https://openalex.org/W4242435873"],"related_works":["https://openalex.org/W1607416738","https://openalex.org/W2524789634","https://openalex.org/W2071668645","https://openalex.org/W2108207895","https://openalex.org/W2216956849","https://openalex.org/W1557932906","https://openalex.org/W2539839227","https://openalex.org/W2023579776","https://openalex.org/W2530948790","https://openalex.org/W2161194249"],"abstract_inverted_index":{"Abstract":[0],"We":[1,82,110,162],"consider":[2],"the":[3,59,63,66,75,79,96,100,105,113,116,126],"problem":[4],"of":[5,65,95,159],"inference":[6,132,142],"for":[7,91,107,134],"nonlinear,":[8],"multivariate":[9],"diffusion":[10,135],"processes,":[11],"satisfying":[12],"It\u00f4":[13],"stochastic":[14],"differential":[15],"equations":[16],"(SDEs),":[17],"using":[18],"data":[19],"at":[20,99,115],"discrete":[21],"times":[22,118],"that":[23,42,89,144,164],"may":[24],"be":[25,72],"incomplete":[26],"and":[27,50,130,148],"subject":[28],"to":[29,47,174],"measurement":[30,60],"error.":[31],"Our":[32],"starting":[33],"point":[34],"is":[35,68,154],"a":[36,85,140],"state-of-the-art":[37],"correlated":[38,44],"pseudo-marginal":[39,128],"Metropolis\u2013Hastings":[40],"algorithm,":[41],"uses":[43],"particle":[45,80],"filters":[46],"induce":[48],"strong":[49],"positive":[51],"correlation":[52,70],"between":[53,125],"successive":[54],"likelihood":[55],"estimates.":[56],"However,":[57],"unless":[58],"error":[61],"or":[62],"dimension":[64],"SDE":[67],"small,":[69],"can":[71],"eroded":[73],"by":[74],"resampling":[76,108],"steps":[77],"in":[78,156,170],"filter.":[81],"therefore":[83],"propose":[84],"novel":[86],"augmentation":[87],"scheme,":[88],"allows":[90],"conditioning":[92],"on":[93],"values":[94],"latent":[97],"process":[98],"observation":[101,117],"times,":[102],"completely":[103],"avoiding":[104],"need":[106],"steps.":[109],"integrate":[111],"over":[112],"uncertainty":[114],"with":[119],"an":[120],"additional":[121],"Gibbs":[122,146],"step.":[123],"Connections":[124],"resulting":[127],"scheme":[129],"existing":[131],"schemes":[133],"processes":[136],"are":[137],"made,":[138],"giving":[139],"unified":[141],"framework":[143],"encompasses":[145],"sampling":[147],"pseudo":[149],"marginal":[150],"schemes.":[151],"The":[152],"methodology":[153],"applied":[155],"three":[157],"examples":[158],"increasing":[160],"complexity.":[161],"find":[163],"our":[165],"approach":[166],"offers":[167],"substantial":[168],"increases":[169],"overall":[171],"efficiency,":[172],"compared":[173],"competing":[175],"methods":[176]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}