@article{oai:nagasaki-u.repo.nii.ac.jp:00016653,
 author = {Masada, Tomonari and Hamada, Tsuyoshi and Shibata, Yuichiro and Oguri, Kiyoshi},
 journal = {Lecture Notes in Computer Science},
 month = {},
 note = {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., Advanced Data Mining and Applications: 5th International Conference, ADMA 2009, Beijing, China, August 17-19, 2009. Proceedings, Lecture Notes in Computer Science, 5678, pp.253-264; 2009},
 pages = {253--264},
 title = {Bayesian multi-topic microarray analysis with hyperparameter reestimation},
 volume = {5678},
 year = {2009}
}