There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean
- a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable;[1]
- procedures in statistical classification to determine class membership probabilities which assess the uncertainty of a given new observation belonging to each of the already established classes.
In addition, calibration is used in statistics with the usual general meaning of calibration. For example, model calibration can be also used to refer to Bayesian inference about the value of a model's parameters, given some data set, or more generally to any type of fitting of a statistical model. As Philip Dawid puts it, "a forecaster is well calibrated if, for example, of those events to which he assigns a probability 30 percent, the long-run proportion that actually occurs turns out to be 30 percent."[2]
- ^ Cook, Ian; Upton, Graham (2006). Oxford Dictionary of Statistics. Oxford: Oxford University Press. ISBN 978-0-19-954145-4.
- ^ Dawid, A. P (1982). "The Well-Calibrated Bayesian". Journal of the American Statistical Association. 77 (379): 605–610. doi:10.1080/01621459.1982.10477856.
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