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Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation

  • Tomonari Masada
  • Tsuyoshi Hamada
  • Yuichiro Shibata
  • Kiyoshi Oguri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5678)

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.

Keywords

Microarray Data Latent Dirichlet Allocation Multinomial Parameter Latent Dirichlet Alloca Hierarchical Dirichlet Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tomonari Masada
    • 1
  • Tsuyoshi Hamada
    • 1
  • Yuichiro Shibata
    • 1
  • Kiyoshi Oguri
    • 1
  1. 1.Nagasaki UniversityNagasakiJapan

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