{"created":"2025-01-19T01:36:47.121883+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234899","sets":["1164:2735:11468:11669"]},"path":["11669"],"owner":"44499","recid":"234899","title":["ゼロ過剰データに対するベイズ符号に基づく因果構造の学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-13"},"_buckets":{"deposit":"378ee06e-3150-4929-b099-3d0dd626ed76"},"_deposit":{"id":"234899","pid":{"type":"depid","value":"234899","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ゼロ過剰データに対するベイズ符号に基づく因果構造の学習","author_link":["640994","640995","640999","640997","640996","640998"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ゼロ過剰データに対するベイズ符号に基づく因果構造の学習"},{"subitem_title":"Causal Structure Learning for Zero-Inflated Data Based on Bayes Code","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報論的学習理論と機械学習2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"東京大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/234899/files/IPSJ-MPS24148012.pdf","label":"IPSJ-MPS24148012.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MPS24148012.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"f2d556d6-c8a2-40e8-8e5c-5aa08d5a03f9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小林, 将理"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"久保木, 優太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松島, 慎"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masatoshi, Kobayashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuta, Kuboki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shin, Matsushima","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10505667","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8833","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，zero inflated な多変量カウントデータに対して，変数間の因果関係を統計的に推論する手法を提案する．既存研究の Choi et al. (2020) が提案した ZIPBN は，因果に Zero-Inflated Poisson (ZIP) モデルを仮定し，因果グラフの識別可能性を利用した手法である．我々は ZIPBN を情報理論的観点から再解釈し，アルゴリズム的独立性の仮定の下でコルモゴロフ複雑性の近似としてのベイズ符号を最小にする因果モデルの選択問題として定式化できることを示す．提案手法の有効性を人工データで検証した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a method for statistically inferring causal relationships between variables in zero-inflated multivariate count data. The Zero-Inflated Poisson Bayesian Network (ZIPBN) model proposed by Choi et al. (2020) assumes a Zero-Inflated Poisson (ZIP) model for causal mechanism and utilizes the identifiability of causal graphs. We reinterpret ZIPBN from an information-theoretic perspective, demonstrating that it can be formulated as a problem of selecting a causal model that minimizes the Bayes Code as an approximation of Kol- mogorov complexity, under the assumption of algorithmic independence of conditionals. The effectiveness of our approach is validated with synthetic data.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告数理モデル化と問題解決（MPS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2024-MPS-148"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234899,"updated":"2025-01-19T09:40:37.899745+00:00","links":{}}