In probability theory and statistics, the covariance function describes how much two random variables change together (their covariance) with varying spatial or temporal separation. For a random field or stochastic process Z(x) on a domain D, a covariance function C(x, y) gives the covariance of the values of the random field at the two locations x and y: C ( x , y ) := cov ( Z ( x ) , Z ( y ) ) = E [ ( Z ( x ) − E [ Z ( x ) ] ) ( Z ( y ) − E [ Z ( y ) ] ) ] .
In probability theory and statistics, the covariance function describes how much two random variables change together (their covariance) with varying spatial or temporal separation. For a random field or stochastic process Z(x) on a domain D, a covariance function C(x, y) gives the covariance of the values of the random field at the two locations x and y: C ( x , y ) := cov ( Z ( x ) , Z ( y ) ) = E [ ( Z ( x ) − E [ Z ( x ) ] ) ( Z ( y ) − E [ Z ( y ) ] ) ] .
Source: Wikipedia "Covariance function" · CC BY-SA 4.0
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