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Bayesian multi-topic microarray analysis with hyperparameter reestimation


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Title: Bayesian multi-topic microarray analysis with hyperparameter reestimation
Authors: Masada, Tomonari / Hamada, Tsuyoshi / Shibata, Yuichiro / Oguri, Kiyoshi
Issue Date: 2009
Publisher: Springer Berlin
Citation: Lecture Notes in Computer Science, 5678, pp.253-264; 2009
Abstract: 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.
Description: 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: 978-3-642-03348-3
ISSN: 03029743
DOI: 10.1007/978-3-642-03348-3_26
Rights: © 2009 Springer. / The original publication is available at www.springerlink.com
Type: Journal Article
Text Version: author
Appears in Collections:Articles in academic journal

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

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