{"id":"https://openalex.org/W4403686922","doi":"https://doi.org/10.1080/10618600.2024.2416521","title":"Efficient Sampling From the Watson Distribution in Arbitrary Dimensions","display_name":"Efficient Sampling From the Watson Distribution in Arbitrary Dimensions","publication_year":2024,"publication_date":"2024-10-23","ids":{"openalex":"https://openalex.org/W4403686922","doi":"https://doi.org/10.1080/10618600.2024.2416521"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2024.2416521","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2416521","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/A5073293874","display_name":"Lukas Sablica","orcid":"https://orcid.org/0000-0001-9166-4563"},"institutions":[{"id":"https://openalex.org/I102248843","display_name":"Vienna University of Economics and Business","ror":"https://ror.org/03yn8s215","country_code":"AT","type":"education","lineage":["https://openalex.org/I102248843"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Lukas Sablica","raw_affiliation_strings":["Institute for Statistics and Mathematics, Vienna University of Economics and Business","Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria"],"raw_orcid":"https://orcid.org/0000-0001-9166-4563","affiliations":[{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business","institution_ids":["https://openalex.org/I102248843"]},{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria","institution_ids":["https://openalex.org/I102248843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078334892","display_name":"Kurt Hornik","orcid":"https://orcid.org/0000-0003-4198-9911"},"institutions":[{"id":"https://openalex.org/I102248843","display_name":"Vienna University of Economics and Business","ror":"https://ror.org/03yn8s215","country_code":"AT","type":"education","lineage":["https://openalex.org/I102248843"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Kurt Hornik","raw_affiliation_strings":["Institute for Statistics and Mathematics, Vienna University of Economics and Business","Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria"],"raw_orcid":"https://orcid.org/0000-0003-4198-9911","affiliations":[{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business","institution_ids":["https://openalex.org/I102248843"]},{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria","institution_ids":["https://openalex.org/I102248843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039117761","display_name":"Josef Leydold","orcid":"https://orcid.org/0000-0002-9076-4893"},"institutions":[{"id":"https://openalex.org/I102248843","display_name":"Vienna University of Economics and Business","ror":"https://ror.org/03yn8s215","country_code":"AT","type":"education","lineage":["https://openalex.org/I102248843"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Josef Leydold","raw_affiliation_strings":["Institute for Statistics and Mathematics, Vienna University of Economics and Business","Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria"],"raw_orcid":"https://orcid.org/0000-0002-9076-4893","affiliations":[{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business","institution_ids":["https://openalex.org/I102248843"]},{"raw_affiliation_string":"Institute for Statistics and Mathematics, Vienna University of Economics and Business, Austria","institution_ids":["https://openalex.org/I102248843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073293874"],"corresponding_institution_ids":["https://openalex.org/I102248843"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16116544,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":"3","first_page":"923","last_page":"933"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.998199999332428,"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":0.998199999332428,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9857000112533569,"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/T10968","display_name":"Statistical Distribution Estimation and Applications","score":0.9828000068664551,"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/watson","display_name":"Watson","score":0.5544337034225464},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5211769938468933},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48725762963294983},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.47586244344711304},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.41975295543670654},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.36009714007377625},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.355236291885376},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35276880860328674},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3343650698661804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2299925982952118},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.14984381198883057},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12337809801101685}],"concepts":[{"id":"https://openalex.org/C2776608531","wikidata":"https://www.wikidata.org/wiki/Q12253","display_name":"Watson","level":2,"score":0.5544337034225464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5211769938468933},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48725762963294983},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.47586244344711304},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.41975295543670654},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.36009714007377625},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.355236291885376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35276880860328674},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3343650698661804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2299925982952118},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.14984381198883057},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12337809801101685},{"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/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10618600.2024.2416521","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2416521","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"},{"id":"pmh:oai:research.wu.ac.at:openaire_cris_publications/88470311-ff3d-403d-90dc-81e1b1882695","is_oa":false,"landing_page_url":"https://research.wu.ac.at/de/publications/88470311-ff3d-403d-90dc-81e1b1882695","pdf_url":null,"source":{"id":"https://openalex.org/S7407055123","display_name":"WU Research","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sablica, L, Hornik, K & Leydold, J 2024, 'Efficient Sampling From the Watson Distribution in Arbitrary Dimensions', Journal of Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2024.2416521","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W105108737","https://openalex.org/W1514329737","https://openalex.org/W1572134371","https://openalex.org/W1973437201","https://openalex.org/W1985339837","https://openalex.org/W1991951515","https://openalex.org/W2013182746","https://openalex.org/W2018148679","https://openalex.org/W2020125737","https://openalex.org/W2029563711","https://openalex.org/W2052879155","https://openalex.org/W2062155244","https://openalex.org/W2078468453","https://openalex.org/W2080094394","https://openalex.org/W2084008613","https://openalex.org/W2108681805","https://openalex.org/W2152828142","https://openalex.org/W2159325249","https://openalex.org/W2204383650","https://openalex.org/W2766967152","https://openalex.org/W2887481058","https://openalex.org/W2963744971","https://openalex.org/W4200274051","https://openalex.org/W4246202668","https://openalex.org/W4246923574","https://openalex.org/W4293067994","https://openalex.org/W6784437763"],"related_works":["https://openalex.org/W4238714840","https://openalex.org/W2049791232","https://openalex.org/W3128679398","https://openalex.org/W2330004501","https://openalex.org/W2137544665","https://openalex.org/W2017089693","https://openalex.org/W2067626990","https://openalex.org/W2058052669","https://openalex.org/W1567644694","https://openalex.org/W2742914308"],"abstract_inverted_index":{"In":[0],"this":[1,165],"article,":[2],"we":[3,44],"present":[4],"two":[5,117],"efficient":[6,132],"methods":[7],"for":[8,32,49,133,164],"sampling":[9,23,54,93,109],"from":[10,25,94],"the":[11,21,41,50,68,116,127,140,156],"Watson":[12,42],"distribution":[13],"in":[14,81,103,155],"arbitrary":[15],"dimensions.":[16,83],"The":[17,84],"first":[18,128],"method":[19,129],"adapts":[20],"rejection":[22,92],"algorithm":[24,99],"Kent,":[26],"Ganeiber,":[27],"and":[28,60,106,147],"Mardia,":[29],"originally":[30],"designed":[31],"Bingham":[33],"distributions,":[34],"using":[35],"angular":[36],"central":[37],"Gaussian":[38],"envelopes.":[39],"For":[40],"distribution,":[43],"derive":[45],"a":[46,73,95],"closed-form":[47],"expression":[48],"parameters":[51],"that":[52,121],"maximize":[53],"efficiency,":[55],"which":[56],"is":[57,100,130],"further":[58],"investigated":[59],"bounded":[61],"by":[62],"asymptotic":[63],"results.":[64],"This":[65,98],"approach":[66],"avoids":[67],"curse":[69],"of":[70],"dimensionality":[71],"through":[72],"smart":[74],"matrix":[75],"inversion,":[76],"enabling":[77],"fast":[78],"runtimes":[79],"even":[80],"high":[82],"second":[85,141],"method,":[86],"based":[87],"on":[88,160],"Saw,":[89],"employs":[90],"adaptive":[91],"projected":[96],"distribution.":[97],"also":[101],"effective":[102],"all":[104],"dimensions":[105],"offers":[107],"rapid":[108],"capabilities.":[110],"Finally,":[111],"our":[112],"simulation":[113],"study":[114],"compares":[115],"main":[118],"methods,":[119],"revealing":[120],"each":[122],"excels":[123],"under":[124],"different":[125],"conditions:":[126],"more":[131,148],"small":[134],"samples":[135,146],"or":[136],"large":[137],"dimensions,":[138],"while":[139],"performs":[142],"better":[143],"with":[144],"larger":[145],"concentrated":[149],"distributions.":[150],"Both":[151],"algorithms":[152],"are":[153,167],"available":[154,168],"R":[157],"package":[158],"watson":[159],"CRAN.":[161],"Supplementary":[162],"materials":[163],"article":[166],"online.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
