A Bayes' factor is a number that quantifies the relative likelihood of two models or hypotheses to each other if made into a ratio e.g. if two models are equally likely based on the prior evidence ( or there is no prior evidence) then the Bayes factor would be one.
Such factors have several uses in algorithms for radiology including image segmentation algorithms but also many newer AI algorithms. When used in image segmentation algorithms the Bayes factor is often used to determine the probability that a particular pixel or voxel belongs to one organ or another.
History and etymology
The Bayes factor was actually not devised by Thomas Bayes, the famous statistician on whose work it is based. The idea behind what is now called the Bayes factor was first articulated by the statistician Sir Harold Jeffreys (1891– 1989) in his book entitled Theory of Probability, and later given the name Bayes factor.
- 1. Robert E. Kass & Adrian E. Raftery (1995) Bayes Factors, Journal of the American Statistical Association, 90:430, 773-795, DOI: 10.1080/01621459.1995.10476572
- 2. Jeffreys H. The theory of probability. 3rd ed. Oxford, England: Oxford University Press, Clarendon Press, 1961.