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Bayesian multi-topic microarray analysis with hyperparameter reestimation

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タイトル: Bayesian multi-topic microarray analysis with hyperparameter reestimation
著者: Masada, Tomonari / Hamada, Tsuyoshi / Shibata, Yuichiro / Oguri, Kiyoshi
発行日: 2009年
出版者: Springer Berlin
引用: Lecture Notes in Computer Science, 5678, pp.253-264; 2009
抄録: 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.
記述: Advanced Data Mining and Applications: 5th International Conference, ADMA 2009, Beijing, China, August 17-19, 2009. Proceedings
URI: http://hdl.handle.net/10069/22537
ISBN: 10.1007/978-3-642-03348-3
ISSN: 03029743
DOI: 10.1007/978-3-642-03348-3_26
権利: © 2009 Springer. / The original publication is available at www.springerlink.com
資料タイプ: Journal Article
原稿種類: author
出現コレクション:060 学術雑誌論文

引用URI : http://hdl.handle.net/10069/22537



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