Bayes Theorem: Difference between revisions

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'''Bayes Theorem''' is defined as "a theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihoods of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result".<ref name="MeSH">{{cite web |url=http://www.nlm.nih.gov/cgi/mesh/2008/MB_cgi?mode= |title=Bayes Theorem |accessdate=2007-12-09 |author=National Library of Medicine |authorlink= |coauthors= |date= |format= |work= |publisher= |pages= |language= |archiveurl= |archivedate= |quote=}}</ref>
'''Bayes Theorem''' is a theorem in [[probability theory]] named for [[Thomas Bayes]] (1702&ndash;1761). In [[epidemiology]], it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihoods of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result".<ref name="MeSH">{{cite web |url=http://www.nlm.nih.gov/cgi/mesh/2008/MB_cgi?mode= |title=Bayes Theorem |accessdate=2007-12-09 |author=National Library of Medicine |authorlink= |coauthors= |date= |format= |work= |publisher= |pages= |language= |archiveurl= |archivedate= |quote=}}</ref>


==Calculations==
==Calculations==

Revision as of 19:36, 19 December 2007

Bayes Theorem is a theorem in probability theory named for Thomas Bayes (1702–1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihoods of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result".[1]

Calculations

For more information, see: Sensitivity and specificity.


References

  1. National Library of Medicine. Bayes Theorem. Retrieved on 2007-12-09.