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  2. Inverse distribution - Wikipedia

    en.wikipedia.org/wiki/Inverse_distribution

    In general, given the probability distribution of a random variable X with strictly positive support, it is possible to find the distribution of the reciprocal, Y = 1 / X. If the distribution of X is continuous with density function f(x) and cumulative distribution function F(x), then the cumulative distribution function, G(y), of the ...

  3. Distributive property - Wikipedia

    en.wikipedia.org/wiki/Distributive_property

    In mathematics, the distributive property of binary operations is a generalization of the distributive law, which asserts that the equality (+) = + is always true in elementary algebra. For example, in elementary arithmetic , one has 2 ⋅ ( 1 + 3 ) = ( 2 ⋅ 1 ) + ( 2 ⋅ 3 ) . {\displaystyle 2\cdot (1+3)=(2\cdot 1)+(2\cdot 3).}

  4. Characteristic function (probability theory) - Wikipedia

    en.wikipedia.org/wiki/Characteristic_function...

    That is, for any two random variables X 1, X 2, both have the same probability distribution if and only if =. [ citation needed ] If a random variable X has moments up to k -th order, then the characteristic function φ X is k times continuously differentiable on the entire real line.

  5. FOIL method - Wikipedia

    en.wikipedia.org/wiki/FOIL_method

    The FOIL method is a special case of a more general method for multiplying algebraic expressions using the distributive law. The word FOIL was originally intended solely as a mnemonic for high-school students learning algebra. The term appears in William Betz's 1929 text Algebra for Today, where he states: [2]

  6. Distribution (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Distribution_(mathematics)

    The support of the distribution associated with the Dirac measure at a point is the set {}. [12] If the support of a test function does not intersect the support of a distribution T then = A distribution T is 0 if and

  7. Convergence of random variables - Wikipedia

    en.wikipedia.org/wiki/Convergence_of_random...

    As an example one may consider random variables with densities f n (x) = (1 + cos(2πnx))1 (0,1). These random variables converge in distribution to a uniform U(0, 1), whereas their densities do not converge at all. [3] However, according to Scheffé’s theorem, convergence of the probability density functions implies convergence in ...