{"id":"https://openalex.org/W2798869818","doi":"https://doi.org/10.1007/s11222-019-09857-1","title":"Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation","display_name":"Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation","publication_year":2019,"publication_date":"2019-02-15","ids":{"openalex":"https://openalex.org/W2798869818","doi":"https://doi.org/10.1007/s11222-019-09857-1","mag":"2798869818"},"language":"en","primary_location":{"id":"doi:10.1007/s11222-019-09857-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-019-09857-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-019-09857-1.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":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11222-019-09857-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059820079","display_name":"Marco Scutari","orcid":"https://orcid.org/0000-0002-2151-7266"},"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":true,"raw_author_name":"Marco Scutari","raw_affiliation_strings":["Department of Statistics, University of Oxford, 24\u201329 St. Giles\u2019, Oxford, OX1 3LB, UK","Department of Statistics, University of Oxford Oxford UK"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Oxford, 24\u201329 St. Giles\u2019, Oxford, OX1 3LB, UK","institution_ids":["https://openalex.org/I40120149"]},{"raw_affiliation_string":"Department of Statistics, University of Oxford Oxford UK","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014282583","display_name":"Claudia Vitolo","orcid":"https://orcid.org/0000-0002-4252-1176"},"institutions":[{"id":"https://openalex.org/I154986956","display_name":"European Centre for Medium-Range Weather Forecasts","ror":"https://ror.org/014w0fd65","country_code":"GB","type":"other","lineage":["https://openalex.org/I154986956"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Claudia Vitolo","raw_affiliation_strings":["Forecast Department, European Centre for Medium-range Weather Forecast, Reading, UK"],"affiliations":[{"raw_affiliation_string":"Forecast Department, European Centre for Medium-range Weather Forecast, Reading, UK","institution_ids":["https://openalex.org/I154986956"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004397914","display_name":"Allan Tucker","orcid":"https://orcid.org/0000-0001-5105-3506"},"institutions":[{"id":"https://openalex.org/I59433898","display_name":"Brunel University of London","ror":"https://ror.org/00dn4t376","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I59433898"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Allan Tucker","raw_affiliation_strings":["Department of Computer Science, Brunel University London, Kingston Lane, Uxbridge, UK","Department of Computer Science Brunel\u00a0University London Uxbridge UK"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Brunel University London, Kingston Lane, Uxbridge, UK","institution_ids":["https://openalex.org/I59433898"]},{"raw_affiliation_string":"Department of Computer Science Brunel\u00a0University London Uxbridge UK","institution_ids":["https://openalex.org/I59433898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059820079"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":{"value":2090,"currency":"EUR","value_usd":2690},"apc_paid":{"value":2090,"currency":"EUR","value_usd":2690},"fwci":0.8671,"has_fulltext":true,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80089197,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"29","issue":"5","first_page":"1095","last_page":"1108"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998999834060669,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998999834060669,"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/T11719","display_name":"Data Quality and Management","score":0.9621999859809875,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9046000242233276,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/bayesian-network","display_name":"Bayesian network","score":0.6881825923919678},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.600848376750946},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5755593776702881},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5475883483886719},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.48171883821487427},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.48090100288391113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4711049199104309},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4682878851890564},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.425730437040329},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36242032051086426},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2686919569969177},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26847946643829346},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1601252555847168}],"concepts":[{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6881825923919678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.600848376750946},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5755593776702881},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5475883483886719},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.48171883821487427},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.48090100288391113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4711049199104309},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4682878851890564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.425730437040329},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36242032051086426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2686919569969177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26847946643829346},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1601252555847168}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1007/s11222-019-09857-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-019-09857-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-019-09857-1.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:arXiv.org:1804.08137","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.08137","pdf_url":"https://arxiv.org/pdf/1804.08137","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2798869818","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1804.08137.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:v-lib-bura1.brunel.ac.uk:2438/17711","is_oa":false,"landing_page_url":"http://bura.brunel.ac.uk/handle/2438/17711","pdf_url":null,"source":{"id":"https://openalex.org/S4306401473","display_name":"Brunel University Research Archive (BURA) (Brunel University London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I59433898","host_organization_name":"Brunel University of London","host_organization_lineage":["https://openalex.org/I59433898"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"doi:10.48550/arxiv.1804.08137","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1804.08137","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.1007/s11222-019-09857-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11222-019-09857-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11222-019-09857-1.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":[{"id":"https://metadata.un.org/sdg/3","score":0.47999998927116394,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320290","display_name":"University of Oxford","ror":"https://ror.org/052gg0110"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2798869818.pdf","grobid_xml":"https://content.openalex.org/works/W2798869818.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1503703685","https://openalex.org/W1517993545","https://openalex.org/W1523680690","https://openalex.org/W1530964327","https://openalex.org/W1572036098","https://openalex.org/W1763728792","https://openalex.org/W1904009171","https://openalex.org/W1982662484","https://openalex.org/W1987757667","https://openalex.org/W2008906462","https://openalex.org/W2035729201","https://openalex.org/W2038374320","https://openalex.org/W2049633694","https://openalex.org/W2055037429","https://openalex.org/W2055626750","https://openalex.org/W2125621954","https://openalex.org/W2128088446","https://openalex.org/W2144731007","https://openalex.org/W2149337551","https://openalex.org/W2160951788","https://openalex.org/W2161632986","https://openalex.org/W2162826352","https://openalex.org/W2165190832","https://openalex.org/W2168175751","https://openalex.org/W2257086086","https://openalex.org/W2502759836","https://openalex.org/W2560575251","https://openalex.org/W2793991332","https://openalex.org/W2796293253","https://openalex.org/W2964239418","https://openalex.org/W3020913839","https://openalex.org/W4232344051","https://openalex.org/W4302423442"],"related_works":["https://openalex.org/W2963290226","https://openalex.org/W1600745635","https://openalex.org/W1979972221","https://openalex.org/W2974856897","https://openalex.org/W2517195773","https://openalex.org/W1554288356","https://openalex.org/W2979638007","https://openalex.org/W3194671762","https://openalex.org/W2278785635","https://openalex.org/W2901745601","https://openalex.org/W2296552148","https://openalex.org/W3082731212","https://openalex.org/W1942909364","https://openalex.org/W2397582304","https://openalex.org/W2031779765","https://openalex.org/W184185418","https://openalex.org/W2805880533","https://openalex.org/W1514932418","https://openalex.org/W2272187831","https://openalex.org/W3210457622"],"abstract_inverted_index":{"Learning":[0],"the":[1,85,89,102,109,122,145,158,188],"structure":[2,25,95],"of":[3,32,57,81,92,105,111,126,147,157,160,176,190],"Bayesian":[4,93,148],"networks":[5],"from":[6,27,224],"data":[7,28,52,69,221],"is":[8],"known":[9],"to":[10,23,116],"be":[11,152],"a":[12,35,78],"computationally":[13],"challenging,":[14],"NP-hard":[15],"problem.":[16],"The":[17],"literature":[18],"has":[19],"long":[20],"investigated":[21],"how":[22],"perform":[24],"learning":[26,96,150],"containing":[29],"large":[30,43,55,212],"numbers":[31,56],"variables,":[33],"following":[34],"general":[36],"interest":[37],"in":[38,45,97],"high-dimensional":[39],"applications":[40],"(\u201csmall":[41],"n,":[42],"p\u201d)":[44],"systems":[46],"biology":[47],"and":[48,67,182,200,215,218],"genetics.":[49],"More":[50],"recently,":[51],"sets":[53,70,222],"with":[54,108,166],"observations":[58],"(the":[59],"so-called":[60],"\u201cbig":[61],"data\u201d)":[62],"have":[63],"become":[64],"increasingly":[65],"common;":[66],"these":[68,209],"are":[71],"not":[72],"necessarily":[73],"high-dimensional,":[74],"sometimes":[75],"having":[76],"only":[77],"few":[79,167],"tens":[80],"variables":[82],"depending":[83],"on":[84,211,219],"application.":[86],"We":[87,131,207],"revisit":[88],"computational":[90],"complexity":[91,119],"network":[94,149,191],"this":[98],"setting,":[99],"showing":[100],"that":[101,144,172,185],"common":[103,124,138],"choice":[104],"measuring":[106],"it":[107,186],"number":[110],"estimated":[112],"local":[113,164],"distributions":[114,165],"leads":[115],"unrealistic":[117],"time":[118],"estimates":[120],"for":[121,163],"most":[123],"class":[125],"score-based":[127],"algorithms,":[128],"greedy":[129],"search.":[130],"then":[132],"derive":[133],"more":[134],"accurate":[135],"expressions":[136,142],"under":[137],"distributional":[139],"assumptions.":[140],"These":[141],"suggest":[143],"speed":[146],"can":[151],"improved":[153],"by":[154,198],"taking":[155],"advantage":[156],"availability":[159],"closed-form":[161],"estimators":[162],"parents.":[168],"Furthermore,":[169],"we":[170,183],"find":[171],"using":[173],"predictive":[174],"instead":[175],"in-sample":[177],"goodness-of-fit":[178],"scores":[179],"improves":[180,187],"speed;":[181],"confirm":[184],"accuracy":[189],"reconstruction":[192],"as":[193,195],"well,":[194],"previously":[196],"observed":[197],"Chickering":[199],"Heckerman":[201],"(Stat":[202],"Comput":[203],"10:":[204],"55\u201362,":[205],"2000).":[206],"demonstrate":[208],"results":[210],"real-world":[213],"environmental":[214],"epidemiological":[216],"data;":[217],"reference":[220],"available":[223],"public":[225],"repositories.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}