Bayesian Methods for Genomics, Molecular and Systems Biology

Ming-Hui Chen (), Dipak K. Dey (), Peter Müller (), Dongchu Sun () and Keying Ye ()
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Ming-Hui Chen: University of Connecticut, Department of Statistics
Dipak K. Dey: University of Connecticut, Department of Statistics
Peter Müller: The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics
Dongchu Sun: University of Missouri-Columbia, Department of Statistics
Keying Ye: University of Texas at San Antonio, Department of Management Science and Statistics, College of Business

Chapter Chapter 9 in Frontiers of Statistical Decision Making and Bayesian Analysis, 2010, pp 285-325 from Springer

Abstract: 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.

Keywords: Markov Chain Monte Carlo; Bayesian Method; Marginal Likelihood; Incomplete Lineage Sorting; Gaussian Graphical Model (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4419-6944-6_9

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DOI: 10.1007/978-1-4419-6944-6_9

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