{"id":"https://openalex.org/W7082984596","doi":"https://doi.org/10.1587/transfun.2025tap0005","title":"Probability Distribution on Rooted Trees: Generalization from Full Trees","display_name":"Probability Distribution on Rooted Trees: Generalization from Full Trees","publication_year":2025,"publication_date":"2025-09-23","ids":{"openalex":"https://openalex.org/W7082984596","doi":"https://doi.org/10.1587/transfun.2025tap0005"},"language":"en","primary_location":{"id":"doi:10.1587/transfun.2025tap0005","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transfun.2025tap0005","pdf_url":"https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2025TAP0005/_pdf","source":{"id":"https://openalex.org/S166990724","display_name":"IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences","issn_l":"0916-8508","issn":["0916-8508","1745-1337"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2025TAP0005/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yuta NAKAHARA","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuta NAKAHARA","raw_affiliation_strings":["Center for Data Science, Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Data Science, Waseda University","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shota SAITO","orcid":null},"institutions":[{"id":"https://openalex.org/I165735259","display_name":"Gunma University","ror":"https://ror.org/046fm7598","country_code":"JP","type":"education","lineage":["https://openalex.org/I165735259"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shota SAITO","raw_affiliation_strings":["Faculty of Informatics, Gunma University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Informatics, Gunma University","institution_ids":["https://openalex.org/I165735259"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Akira KAMATSUKA","orcid":null},"institutions":[{"id":"https://openalex.org/I197425175","display_name":"Shonan Institute of Technology","ror":"https://ror.org/01bawqf59","country_code":"JP","type":"education","lineage":["https://openalex.org/I197425175"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira KAMATSUKA","raw_affiliation_strings":["Department of Information Science, Shonan Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science, Shonan Institute of Technology","institution_ids":["https://openalex.org/I197425175"]}]},{"author_position":"last","author":{"id":null,"display_name":"Toshiyasu MATSUSHIMA","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshiyasu MATSUSHIMA","raw_affiliation_strings":["Department of Pure and Applied Mathematics, Waseda University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Pure and Applied Mathematics, Waseda University","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.66593503,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"E109.A","issue":"3","first_page":"524","last_page":"537"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6585000157356262,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6585000157356262,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.026000000536441803,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.019500000402331352,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5896000266075134},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5735999941825867},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5690000057220459},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.5317000150680542},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.42969998717308044},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.40299999713897705},{"id":"https://openalex.org/keywords/empirical-probability","display_name":"Empirical probability","score":0.38440001010894775},{"id":"https://openalex.org/keywords/probability-model","display_name":"Probability model","score":0.3831999897956848},{"id":"https://openalex.org/keywords/probability-mass-function","display_name":"Probability mass function","score":0.3725000023841858}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6262999773025513},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5896000266075134},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5690000057220459},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.5317000150680542},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4392000138759613},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.42969998717308044},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C97933134","wikidata":"https://www.wikidata.org/wiki/Q5374249","display_name":"Empirical probability","level":4,"score":0.38440001010894775},{"id":"https://openalex.org/C2984179964","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Probability model","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3797000050544739},{"id":"https://openalex.org/C197096303","wikidata":"https://www.wikidata.org/wiki/Q869887","display_name":"Probability mass function","level":3,"score":0.3725000023841858},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.33070001006126404},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.30979999899864197},{"id":"https://openalex.org/C141042865","wikidata":"https://www.wikidata.org/wiki/Q200125","display_name":"Expected value","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C101796028","wikidata":"https://www.wikidata.org/wiki/Q535587","display_name":"Moment-generating function","level":3,"score":0.3068999946117401},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.30649998784065247},{"id":"https://openalex.org/C108882938","wikidata":"https://www.wikidata.org/wiki/Q274506","display_name":"Uniform distribution (continuous)","level":2,"score":0.3012000024318695},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C2776839635","wikidata":"https://www.wikidata.org/wiki/Q14942679","display_name":"Random tree","level":4,"score":0.2919999957084656},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.2897000014781952},{"id":"https://openalex.org/C27956954","wikidata":"https://www.wikidata.org/wiki/Q391371","display_name":"Bernoulli distribution","level":3,"score":0.2888000011444092},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2770000100135803},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2660999894142151},{"id":"https://openalex.org/C103784038","wikidata":"https://www.wikidata.org/wiki/Q386228","display_name":"Cumulative distribution function","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transfun.2025tap0005","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transfun.2025tap0005","pdf_url":"https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2025TAP0005/_pdf","source":{"id":"https://openalex.org/S166990724","display_name":"IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences","issn_l":"0916-8508","issn":["0916-8508","1745-1337"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1587/transfun.2025tap0005","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transfun.2025tap0005","pdf_url":"https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2025TAP0005/_pdf","source":{"id":"https://openalex.org/S166990724","display_name":"IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences","issn_l":"0916-8508","issn":["0916-8508","1745-1337"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7082984596.pdf","grobid_xml":"https://content.openalex.org/works/W7082984596.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1558603822","https://openalex.org/W2146395539","https://openalex.org/W2156060128","https://openalex.org/W2163294786","https://openalex.org/W2165843147","https://openalex.org/W2295598076","https://openalex.org/W2911964244","https://openalex.org/W3045608583","https://openalex.org/W3168449777","https://openalex.org/W3175260198","https://openalex.org/W3197818463","https://openalex.org/W4214506138","https://openalex.org/W4283809571","https://openalex.org/W4289655391","https://openalex.org/W4289713181"],"related_works":[],"abstract_inverted_index":{"The":[0],"hierarchical":[1,32],"and":[2,24,62,128],"recursive":[3,137],"expressive":[4,33],"capability":[5,34],"of":[6,103,125,143],"rooted":[7,54,117],"trees":[8,118],"is":[9,47,56],"applicable":[10],"to":[11,38,44,100,139],"represent":[12],"statistical":[13],"models":[14],"in":[15,51,119],"various":[16],"areas,":[17],"such":[18,31],"as":[19,58],"data":[20,81],"compression,":[21],"image":[22],"processing,":[23],"machine":[25],"learning.":[26],"On":[27],"the":[28,53,71,75,85,94,101,122,129,141,144],"other":[29],"hand,":[30],"causes":[35],"a":[36,48,59,63,79,111],"problem":[37],"avoid":[39],"overfitting.":[40],"One":[41],"unified":[42],"approach":[43,90],"solve":[45],"this":[46,89,107],"Bayesian":[49],"approach,":[50],"which":[52,120],"tree":[55],"regarded":[57],"random":[60],"variable":[61],"direct":[64],"loss":[65],"function":[66],"can":[67],"be":[68],"assumed":[69],"on":[70,88,93,97],"selected":[72],"model":[73],"or":[74],"predicted":[76],"value":[77],"for":[78,115],"new":[80],"point.":[82],"However,":[83],"all":[84],"previous":[86],"studies":[87],"are":[91,132],"based":[92],"probability":[95,113,145],"distribution":[96,114,146],"full":[98],"trees,":[99],"best":[102],"our":[104],"knowledge.":[105],"In":[106],"paper,":[108],"we":[109,135],"propose":[110],"generalized":[112],"any":[116,148],"only":[121],"maximum":[123,130],"number":[124],"child":[126],"nodes":[127],"depth":[131],"fixed.":[133],"Furthermore,":[134],"derive":[136],"methods":[138],"evaluate":[140],"characteristics":[142],"without":[147],"approximations.":[149]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
