First author: Department of Plant Pathology, University of California-Davis, Kearney Agricultural Center, Parlier 93648; and second author: Department of Statistics, Iowa State University, Ames 50011
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Accepted for publication 16 May 2004.
Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.
© 2004 The American Phytopathological Society