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{{Short description|Correlation as a function of distance}}
{{other uses}}
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[[File:Comparison_convolution_correlation.svg|thumb|300px|Visual comparison of [[convolution]], [[cross-correlation]] and [[autocorrelation]].]]
A '''correlation function''' is a [[function (mathematics)|function]] that gives the statistical [[correlation]] between [[random variable]]s, contingent on the spatial or temporal distance between those variables.<ref>{{cite
| last1=Pal | first1=Manoranjan
| last2=Bharati | first2=Premananda
| date=2019
| chapter=Introduction to Correlation and Linear Regression Analysis
| title=Applications of Regression Techniques
| url=https://link.springer.com/chapter/10.1007/978-981-13-9314-3_1
| publisher=Springer, Singapore
| pages=1–18
| doi=10.1007/978-981-13-9314-3_1
| access-date=December 14, 2023}}</ref> If one considers the correlation function between random variables representing the same quantity measured at two different points, then this is often referred to as an [[autocorrelation function]], which is made up of [[autocorrelation]]s. Correlation functions of different random variables are sometimes called '''cross-correlation functions''' to emphasize that different variables are being considered and because they are made up of [[cross-correlation]]s.
Correlation functions are a useful indicator of dependencies as a function of distance in time or space, and they can be used to assess the distance required between sample points for the values to be effectively uncorrelated. In addition, they can form the basis of rules for interpolating values at points for which there are no observations.
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==Properties of probability distributions==
With these definitions, the study of correlation functions is similar to the study of [[probability distributions]]. Many stochastic processes can be completely characterized by their correlation functions; the most notable example is the class of [[Gaussian processes]].
Probability distributions defined on a finite number of points can always be normalized, but when these are defined over continuous spaces, then extra care is called for. The study of such distributions started with the study of [[random walk]]s and led to the notion of the [[Itō calculus]].
The Feynman [[path integral formulation|path integral]] in Euclidean space generalizes this to other problems of interest to [[statistical mechanics]]. Any probability distribution which obeys a condition on correlation functions called [[reflection positivity]] leads to a local [[quantum field theory]] after [[Wick rotation]] to [[Minkowski spacetime]] (
==See also==
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*{{annotated link|Correlogram}}
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*{{annotated link|Correlation function (quantum field theory)}}
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==References==
{{reflist}}
{{Statistical mechanics topics}}
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[[Category:Covariance and correlation]]
[[Category:Time series]]
[[Category:Spatial
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