2009年
Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS
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- ,
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- 巻
- 5678
- 号
- 開始ページ
- 253
- 終了ページ
- 264
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-642-03348-3_26
- 出版者・発行元
- SPRINGER-VERLAG BERLIN
This paper provides a new method for multi-topic Bayesian analysis for microarray data. Our method achieves a further maximization of lower bounds in a marginalized variational Bayesian inference (MVB) for Latent Process Decomposition (LPD), which is an effective probabilistic model for microarray data. In our method, hyperparameters in LPD are updated by empirical Bayes point estimation. The experiments based on microarray data of realistically large size show efficiency of our hyperparameter reestimation technique.
- リンク情報
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- DOI
- https://doi.org/10.1007/978-3-642-03348-3_26
- DBLP
- https://dblp.uni-trier.de/rec/conf/adma/MasadaHSO09
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000270597500022&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/adma/adma2009.html#conf/adma/MasadaHSO09
- ID情報
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- DOI : 10.1007/978-3-642-03348-3_26
- ISSN : 0302-9743
- DBLP ID : conf/adma/MasadaHSO09
- Web of Science ID : WOS:000270597500022