Bayesian Methods for Genomics, Molecular and Systems Biology

  • Ming-Hui Chen
  • Dipak K. Dey
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
Chapter

Abstract

Inference for high throughput genomic data has emerged as a major source of challenges for statistical inference in general, and Bayesian analysis in particular. This chapter discusses some related current research frontiers. The chapter highlights how specific strengths of the Bayesian approach are important to model such data. Bayesian inference provides a natural paradigm to exploit the considerable prior information that is available about important biological pathways. Another strength of Bayesian inference that leads to research opportunities with phylogenomic data is the natural ease of simultaneous modeling and inference on multiple related processes.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Dipak K. Dey
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Department of BiostatisticsThe University of Texas, M. D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  4. 4.Department of Management Science and Statistics, College of BusinessUniversity of Texas at San AntonioSan AntonioUSA

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