{"id":"https://openalex.org/W3081839442","doi":"https://doi.org/10.1080/10618600.2020.1816177","title":"An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions","display_name":"An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions","publication_year":2020,"publication_date":"2020-09-01","ids":{"openalex":"https://openalex.org/W3081839442","doi":"https://doi.org/10.1080/10618600.2020.1816177","mag":"3081839442"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2020.1816177","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1816177","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-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/A5100341310","display_name":"Qi Wang","orcid":"https://orcid.org/0000-0002-6314-7021"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qi Wang","raw_affiliation_strings":["Department of Statistics, Purdue University, West Lafayette, IN;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Purdue University, West Lafayette, IN;","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083409558","display_name":"Vinayak Rao","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vinayak Rao","raw_affiliation_strings":["Department of Statistics, Purdue University, West Lafayette, IN;"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Purdue University, West Lafayette, IN;","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064373793","display_name":"Yee Whye Teh","orcid":"https://orcid.org/0000-0001-5365-6933"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yee Whye Teh","raw_affiliation_strings":["Department of Statistics, University of Oxford, Oxford, UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Oxford, Oxford, UK","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083409558"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09871394,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":"2","first_page":"297","last_page":"311"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9991000294685364,"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":0.9991000294685364,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9973000288009644,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9943000078201294,"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.6010062098503113},{"id":"https://openalex.org/keywords/rejection-sampling","display_name":"Rejection sampling","score":0.5909143090248108},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.5847200155258179},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5391822457313538},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5201309323310852},{"id":"https://openalex.org/keywords/stochastic-differential-equation","display_name":"Stochastic differential equation","score":0.5143691301345825},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49010810256004333},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.47300058603286743},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.45843085646629333},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.4318532645702362},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.42711925506591797},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.42671650648117065},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41704148054122925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4057347774505615},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.3806935250759125},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.17966896295547485},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17041024565696716},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12994876503944397}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6010062098503113},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.5909143090248108},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.5847200155258179},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5391822457313538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5201309323310852},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.5143691301345825},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49010810256004333},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.47300058603286743},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.45843085646629333},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.4318532645702362},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.42711925506591797},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42671650648117065},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41704148054122925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4057347774505615},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3806935250759125},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.17966896295547485},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17041024565696716},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12994876503944397},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2020.1816177","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2020.1816177","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W130710483","https://openalex.org/W581112773","https://openalex.org/W630922856","https://openalex.org/W1501586228","https://openalex.org/W1925297253","https://openalex.org/W1964568252","https://openalex.org/W1979832306","https://openalex.org/W1982785183","https://openalex.org/W1997628954","https://openalex.org/W2013145865","https://openalex.org/W2014604466","https://openalex.org/W2015510160","https://openalex.org/W2017957151","https://openalex.org/W2035935841","https://openalex.org/W2043719428","https://openalex.org/W2059661250","https://openalex.org/W2064401329","https://openalex.org/W2077791698","https://openalex.org/W2088413985","https://openalex.org/W2097793382","https://openalex.org/W2101270680","https://openalex.org/W2109024167","https://openalex.org/W2114134972","https://openalex.org/W2123622029","https://openalex.org/W2126750122","https://openalex.org/W2138565515","https://openalex.org/W2142832782","https://openalex.org/W2148718972","https://openalex.org/W2154047075","https://openalex.org/W2163715525","https://openalex.org/W2487730110","https://openalex.org/W2495436614","https://openalex.org/W2594722540","https://openalex.org/W2597187653","https://openalex.org/W2604272474","https://openalex.org/W2612972698","https://openalex.org/W2802417157","https://openalex.org/W2954040150","https://openalex.org/W3005130686","https://openalex.org/W3098120542","https://openalex.org/W3101800103","https://openalex.org/W3123019596","https://openalex.org/W4211049957","https://openalex.org/W4238187298","https://openalex.org/W4256170606","https://openalex.org/W4302158866","https://openalex.org/W4302382515","https://openalex.org/W4302617909","https://openalex.org/W4388311561"],"related_works":["https://openalex.org/W4295750535","https://openalex.org/W13281851","https://openalex.org/W2539839227","https://openalex.org/W3097509027","https://openalex.org/W2045761178","https://openalex.org/W105676162","https://openalex.org/W2783865284","https://openalex.org/W2592308920","https://openalex.org/W2059147320","https://openalex.org/W1539868720"],"abstract_inverted_index":{"Stochastic":[0],"differential":[1],"equations":[2],"(SDEs)":[3],"or":[4],"diffusions":[5,89],"are":[6,187],"continuous-valued":[7],"continuous-time":[8],"stochastic":[9],"processes":[10,23],"widely":[11],"used":[12],"in":[13],"the":[14,55,106,114,133,143,152],"applied":[15],"and":[16,29,95,131,171],"mathematical":[17],"sciences.":[18],"Simulating":[19],"paths":[20],"from":[21,60,126,151],"these":[22],"is":[24,52],"usually":[25],"an":[26,36,61,80,98,127],"intractable":[27],"problem,":[28],"typically":[30],"involves":[31,45,117],"time-discretization":[32,48],"approximations.":[33],"We":[34,165],"propose":[35],"exact":[37],"Markov":[38],"chain":[39],"Monte":[40,147],"Carlo":[41,148],"sampling":[42,83,102,149],"algorithm":[43,84,103,116],"that":[44,104],"no":[46],"such":[47],"error.":[49],"Our":[50,77,140],"sampler":[51],"applicable":[53],"to":[54,156,159,161],"problem":[56],"of":[57,88,124,146],"prior":[58],"simulation":[59,64],"SDE,":[62],"posterior":[63],"conditioned":[65,136],"on":[66,137,169],"noisy":[67,75],"observations,":[68],"as":[69,71,90],"well":[70],"parameter":[72],"inference":[73],"given":[74],"observations.":[76],"work":[78,141],"recasts":[79],"existing":[81],"rejection":[82],"for":[85,184],"a":[86,91,111,121],"class":[87],"latent":[92],"variable":[93,100],"model,":[94],"then":[96],"derives":[97],"auxiliary":[99],"Gibbs":[101],"targets":[105],"associated":[107],"joint":[108],"distribution.":[109],"At":[110],"high":[112],"level,":[113],"resulting":[115],"two":[118],"steps:":[119],"simulating":[120],"random":[122],"grid":[123],"times":[125],"inhomogeneous":[128],"Poisson":[129],"process,":[130],"updating":[132],"SDE":[134],"trajectory":[135],"this":[138,185],"grid.":[139],"allows":[142],"vast":[144],"literature":[145,155],"algorithms":[150],"Gaussian":[153],"process":[154],"be":[157],"brought":[158],"bear":[160],"applications":[162],"involving":[163],"diffusions.":[164],"study":[166],"our":[167],"method":[168],"synthetic":[170],"real":[172],"datasets,":[173],"where":[174],"we":[175],"demonstrate":[176],"superior":[177],"performance":[178],"over":[179],"competing":[180],"methods.":[181],"Supplementary":[182],"materials":[183],"article":[186],"available":[188],"online.":[189]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}