{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232856","sets":["934:989:11507:11508"]},"path":["11508"],"owner":"44499","recid":"232856","title":["Bayesian Spectral Analysis with Binomial Distribution Noise"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-28"},"_buckets":{"deposit":"eba580b5-7344-4c8f-beef-211500d2fd3e"},"_deposit":{"id":"232856","pid":{"type":"depid","value":"232856","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Bayesian Spectral Analysis with Binomial Distribution Noise","author_link":["631307","631298","631299","631300","631305","631302","631303","631301","631304","631306"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Bayesian Spectral Analysis with Binomial Distribution Noise"},{"subitem_title":"Bayesian Spectral Analysis with Binomial Distribution Noise","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[オリジナル論文] binomial distribution, Bayesian inference, spectral deconvolution, X-ray emission spectroscopy, absorption spectra","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2024-02-28","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"University of Tokyo"},{"subitem_text_value":"National Institute for Materials Science"},{"subitem_text_value":"University of Tokyo"},{"subitem_text_value":"Kumamoto University"},{"subitem_text_value":"University of Tokyo"}]},"item_3_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":"National Institute for Materials Science","subitem_text_language":"en"},{"subitem_text_value":"University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Kumamoto University","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":"eng"}]},"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/232856/files/IPSJ-TOM1701007.pdf","label":"IPSJ-TOM1701007.pdf"},"date":[{"dateType":"Available","dateValue":"2026-02-28"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1701007.pdf","filesize":[{"value":"11.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5cb8cf11-14f9-4f74-9357-d39e715c9f18","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomohiro, Nabika"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenji, Nagata"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shun, Katakami"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaichiro, Mizumaki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Okada"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomohiro, Nabika","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kenji, Nagata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shun, Katakami","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaichiro, Mizumaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Okada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In some spectral measurements, such as absorption spectra, data are obtained as observation rates. When analyzing such data, Gaussian noise is typically assumed. However, the process of data generation can be modeled with binomial distribution noise. Conversely, in Bayesian analysis for spectral measurements, selecting an appropriate noise model is important. Therefore, we developed Bayesian spectral deconvolution based on a binomial distribution and compared it with Bayesian spectral deconvolution based on a Gaussian distribution. Using artificial data, we show that different noise models change the posterior distribution of peak numbers and their parameters, thereby affecting the analysis results. Moreover, we found that Bayesian spectral deconvolution based on a binomial distribution can analyze data with flattened peak structures, which was previously impossible to analyze. Using real data from X-ray emission spectroscopy, we confirmed that binomial distribution noise is more appropriate than Gaussian noise by Bayesian inference.","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In some spectral measurements, such as absorption spectra, data are obtained as observation rates. When analyzing such data, Gaussian noise is typically assumed. However, the process of data generation can be modeled with binomial distribution noise. Conversely, in Bayesian analysis for spectral measurements, selecting an appropriate noise model is important. Therefore, we developed Bayesian spectral deconvolution based on a binomial distribution and compared it with Bayesian spectral deconvolution based on a Gaussian distribution. Using artificial data, we show that different noise models change the posterior distribution of peak numbers and their parameters, thereby affecting the analysis results. Moreover, we found that Bayesian spectral deconvolution based on a binomial distribution can analyze data with flattened peak structures, which was previously impossible to analyze. Using real data from X-ray emission spectroscopy, we confirmed that binomial distribution noise is more appropriate than Gaussian noise by Bayesian inference.","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"56","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"47","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"17"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":232856,"updated":"2025-01-19T10:17:54.485591+00:00","links":{},"created":"2025-01-19T01:33:58.084336+00:00"}