| Item type |
Trans(1) |
| 公開日 |
2024-02-28 |
| タイトル |
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タイトル |
Bayesian Spectral Analysis with Binomial Distribution Noise |
| タイトル |
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言語 |
en |
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タイトル |
Bayesian Spectral Analysis with Binomial Distribution Noise |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
[オリジナル論文] binomial distribution, Bayesian inference, spectral deconvolution, X-ray emission spectroscopy, absorption spectra |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者所属 |
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University of Tokyo |
| 著者所属 |
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National Institute for Materials Science |
| 著者所属 |
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University of Tokyo |
| 著者所属 |
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Kumamoto University |
| 著者所属 |
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University of Tokyo |
| 著者所属(英) |
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en |
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University of Tokyo |
| 著者所属(英) |
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en |
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National Institute for Materials Science |
| 著者所属(英) |
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en |
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University of Tokyo |
| 著者所属(英) |
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en |
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Kumamoto University |
| 著者所属(英) |
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en |
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University of Tokyo |
| 著者名 |
Tomohiro, Nabika
Kenji, Nagata
Shun, Katakami
Masaichiro, Mizumaki
Masato, Okada
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| 著者名(英) |
Tomohiro, Nabika
Kenji, Nagata
Shun, Katakami
Masaichiro, Mizumaki
Masato, Okada
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
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. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
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. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA11464803 |
| 書誌情報 |
情報処理学会論文誌数理モデル化と応用（TOM）
巻 17,
号 1,
p. 47-56,
発行日 2024-02-28
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
1882-7780 |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-TOM1701007.pdf (11.1 MB)
