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The duality between covariance and contravariance intervenes whenever a vector or tensor quantity is represented by its components, although modern differential geometry uses more sophisticated index-free methods to represent tensors. In tensor analysis, a covariant vector varies more or less reciprocally to a corresponding contravariant vector ...
By the usual subtyping rule for function types, this means that the overriding method should return a more specific type (return type covariance) and accept a more general argument (parameter type contravariance). In UML notation, the possibilities are as follows (where Class B is the subclass that extends Class A which is the superclass):
Covariance and contravariance may refer to: Covariance and contravariance of vectors, in mathematics and theoretical physics; Covariance and contravariance of functors, in category theory; Covariance and contravariance (computer science), whether a type system preserves the ordering ≤ of types; An informal synonym for invariance (physics)
The sign of the covariance of two random variables X and Y. In probability theory and statistics, covariance is a measure of the joint variability of two random variables. [1] The sign of the covariance, therefore, shows the tendency in the linear relationship between the variables.
The explicit form of a covariant transformation is best introduced with the transformation properties of the derivative of a function. Consider a scalar function f (like the temperature at a location in a space) defined on a set of points p, identifiable in a given coordinate system , =,, … (such a collection is called a manifold).
With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.
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 ...
The article describes covariance and contravariance in terms of coordinates and components, a perspective that is rather dated. The terms have a meaning independent of any choice of basis or coordinates, and the article should reflect that. There is a lot of variation in the literature, but essentially there are three styles: