 Correlation function

For other uses, see Correlation function (disambiguation).
A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points. 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 being made up of autocorrelations. Correlation functions of different random variables are sometimes called cross correlation functions to emphasise that different variables are being considered and because they are made up of cross correlations.
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 observations.
Correlation functions used in astronomy, financial analysis, and statistical mechanics differ only in the particular stochastic processes they are applied to. In quantum field theory there are correlation functions over quantum distributions.
Definition
For random variables X(s) and X(t) at different points s and t of some space, the correlation function is
where is described in the article on correlation. In this definition, it has been assumed that the stochastic variable is scalarvalued. If it is not, then more complicated correlation functions can be defined. For example, if one has a vector X_{i}(s), then one can define the matrix of correlation functions
or a scalar, which is the trace of this matrix. If the probability distribution has any target space symmetries, i.e. symmetries in the space of the stochastic variable (also called internal symmetries), then the correlation matrix will have induced symmetries. If there are symmetries of the space (or time) in which the random variables exist (also called spacetime symmetries) then the correlation matrix will have special properties. Examples of important spacetime symmetries are —
 translational symmetry yields C(s,s') = C(s − s') where s and s' are to be interpreted as vectors giving coordinates of the points
 rotational symmetry in addition to the above gives C(s, s') = C(s − s') where x denotes the norm of the vector x (for actual rotations this is the Euclidean or 2norm).
n is
If the random variable has only one component, then the indices i_{j} are redundant. If there are symmetries, then the correlation function can be broken up into irreducible representations of the symmetries — both internal and spacetime.
The case of correlations of a single random variable can be thought of as a special case of autocorrelation of a stochastic process on a space which contains a single point.
Properties of probability distributions
With these definitions, the study of correlation functions is equivalent to the study of probability distributions.^{[clarification needed]} 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 walks and led to the notion of the Ito calculus.
The Feynman 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 lead to a local quantum field theory after Wick rotation to Minkowski spacetime. The operation of renormalization is a specified set of mappings from the space of probability distributions to itself. A quantum field theory is called renormalizable if this mapping has a fixed point which gives a quantum field theory.
See also
Categories: Covariance and correlation
 Time series analysis
 Spatial data analysis
 Types of function
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