{"id":"https://openalex.org/W7169123896","doi":"https://doi.org/10.1016/j.ijar.2026.109771","title":"A BGe score for tied-covariance mixture Gaussian Bayesian networks","display_name":"A BGe score for tied-covariance mixture Gaussian Bayesian networks","publication_year":2026,"publication_date":"2026-07-16","ids":{"openalex":"https://openalex.org/W7169123896","doi":"https://doi.org/10.1016/j.ijar.2026.109771"},"language":"en","primary_location":{"id":"doi:10.1016/j.ijar.2026.109771","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ijar.2026.109771","pdf_url":null,"source":{"id":"https://openalex.org/S33368595","display_name":"International Journal of Approximate Reasoning","issn_l":"0888-613X","issn":["0888-613X","1873-4731"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Approximate Reasoning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.ijar.2026.109771","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068721139","display_name":"Marco Grzegorczyk","orcid":"https://orcid.org/0000-0002-2604-9270"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Marco Grzegorczyk","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2604-9270","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5068721139"],"corresponding_institution_ids":[],"apc_list":{"value":2960,"currency":"USD","value_usd":2960},"apc_paid":{"value":2960,"currency":"USD","value_usd":2960},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78429941,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"198","issue":null,"first_page":"109771","last_page":"109771"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.33230000734329224,"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":0.33230000734329224,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.227400004863739,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.16689999401569366,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6183000206947327},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5504999756813049},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.45489999651908875},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.44780001044273376},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4011000096797943}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6315000057220459},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5504999756813049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5450999736785889},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.45489999651908875},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36640000343322754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2842999994754791},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2766000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.27000001072883606}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.ijar.2026.109771","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ijar.2026.109771","pdf_url":null,"source":{"id":"https://openalex.org/S33368595","display_name":"International Journal of Approximate Reasoning","issn_l":"0888-613X","issn":["0888-613X","1873-4731"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Approximate Reasoning","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2511.07050","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2511.07050","pdf_url":"https://arxiv.org/pdf/2511.07050","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1016/j.ijar.2026.109771","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ijar.2026.109771","pdf_url":null,"source":{"id":"https://openalex.org/S33368595","display_name":"International Journal of Approximate Reasoning","issn_l":"0888-613X","issn":["0888-613X","1873-4731"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Approximate Reasoning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1517637854","https://openalex.org/W1972555382","https://openalex.org/W1975120776","https://openalex.org/W1984567898","https://openalex.org/W2001619934","https://openalex.org/W2015245929","https://openalex.org/W2030680205","https://openalex.org/W2143943607","https://openalex.org/W2144731007","https://openalex.org/W2157955324","https://openalex.org/W2168175751","https://openalex.org/W2170112109","https://openalex.org/W2267567366","https://openalex.org/W2330192890","https://openalex.org/W2950017290","https://openalex.org/W2979638007","https://openalex.org/W2999231652","https://openalex.org/W3111369083","https://openalex.org/W3196873500","https://openalex.org/W3201412947","https://openalex.org/W4226399656","https://openalex.org/W4381251329","https://openalex.org/W4385900863","https://openalex.org/W4394770197","https://openalex.org/W4396216837","https://openalex.org/W4413384881","https://openalex.org/W4415696907"],"related_works":[],"abstract_inverted_index":{"Mixtures":[0],"of":[1,43,79,109],"Gaussian":[2,80,110],"Bayesian":[3,81,111],"networks":[4,112],"have":[5],"previously":[6],"been":[7],"studied":[8],"under":[9],"full-covariance":[10,53,107],"assumptions,":[11],"where":[12],"each":[13],"mixture":[14,24],"component":[15],"has":[16],"its":[17,44],"own":[18],"covariance":[19,35],"matrix.":[20,36],"We":[21,65],"propose":[22],"a":[23,33,94],"model":[25,84],"with":[26,93],"tied-covariance,":[27],"in":[28,51],"which":[29,47],"all":[30],"components":[31],"share":[32],"common":[34],"Our":[37],"main":[38],"contribution":[39],"is":[40],"the":[41,52,56,68,72,104],"derivation":[42],"marginal":[45,57],"likelihood,":[46],"remains":[48],"analytic.":[49],"Unlike":[50],"case,":[54],"however,":[55],"likelihood":[58,70],"no":[59],"longer":[60],"factorizes":[61],"into":[62],"component-specific":[63],"terms.":[64],"refer":[66],"to":[67],"new":[69],"as":[71],"BGe":[73],"scoring":[74],"metric":[75],"for":[76,98],"tied-covariance":[77],"mixtures":[78,108],"networks.":[82],"For":[83],"inference,":[85],"we":[86,101],"implement":[87],"MCMC":[88,92],"schemes":[89],"combining":[90],"structure":[91],"fast":[95],"Gibbs":[96],"sampler":[97],"mixtures,":[99],"and":[100,106,115],"empirically":[102],"compare":[103],"tied-":[105],"on":[113],"simulated":[114],"benchmark":[116],"data.":[117]},"counts_by_year":[],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2026-07-17T00:00:00"}
