Characteristic function

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In mathematics, the term "characteristic function" can refer to any of several distinct concepts:

[math]\displaystyle{ \mathbf{1}_A\colon X \to \{0, 1\}, }[/math]
which for a given subset A of X, has value 1 at points of A and 0 at points of X − A.
  • There is an indicator function for affine varieties over a finite field:[1] given a finite set of functions [math]\displaystyle{ f_\alpha \in \mathbb{F}_q[x_1,\ldots,x_n] }[/math] let [math]\displaystyle{ V = \{ x \in \mathbb{F}_q^n : f_\alpha(x) = 0 \} }[/math] be their vanishing locus. Then, the function [math]\displaystyle{ P(x) = \prod(1 - f_\alpha(x)^{q-1}) }[/math] acts as an indicator function for [math]\displaystyle{ V }[/math]. If [math]\displaystyle{ x \in V }[/math] then [math]\displaystyle{ P(x) = 1 }[/math], otherwise, for some [math]\displaystyle{ f_\alpha }[/math], we have [math]\displaystyle{ f_\alpha(x) \neq 0 }[/math], which implies that [math]\displaystyle{ f_\alpha(x)^{q-1} = 1 }[/math], hence [math]\displaystyle{ P(x) = 0 }[/math].
  • The characteristic function in convex analysis, closely related to the indicator function of a set:
[math]\displaystyle{ \chi_A (x) := \begin{cases} 0, & x \in A; \\ + \infty, & x \not \in A. \end{cases} }[/math]
  • In probability theory, the characteristic function of any probability distribution on the real line is given by the following formula, where X is any random variable with the distribution in question:
[math]\displaystyle{ \varphi_X(t) = \operatorname{E}\left(e^{itX}\right), }[/math]
where E means expected value. For multivariate distributions, the product tX is replaced by a scalar product of vectors.

References

  1. Serre. Course in Arithmetic. pp. 5.