Open AccessBayesian Analysis in Plant Pathology
Author(s) -
A. L. Mila,
Alicia L. Carriquiry
Publication year - 2004
Publication title -
phytopathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.264
H-Index - 131
eISSN - 1943-7684
pISSN - 0031-949X
DOI - 10.1094/phyto.2004.94.9.1027
Subject(s) - bayesian probability , bayesian inference , inference , biology , bayesian statistics , computational biology , set (abstract data type) , bayesian network , variable order bayesian network , computer science , artificial intelligence , machine learning , programming language
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.