See also moment (physics).

The concept of moment in mathematics evolved from the concept of moment in physics. The nth moment of a real-valued function f(x) of a real variable is

The problem of moments seeks characterizations of sequences { μ′_n : n = 1, 2, 3, ... } that are sequences of moments of some function f.

If (lower-case) f is a probability density function, then the value integral above is called the nth moment of the probability distribution. More generally, if (capital) F is a cumulative probability distribution function of any probability distribution, which may not have a density function, then the nth moment of the probability distribution is given by the Riemann-Stieltjes integral

where X is a random variable that has this distribution.

The nth central moment of the probability distribution of a random variable X is

μ_n = E((X - μ₁')ⁿ).

The second central moment is the variance.

The central moments are clearly translation-invariant, i.e., the nth central moment of X is the same as that of X + c for any constant c (in this context "constant" means a non-random quantity).

The first moment and the second and third central moments are linear in the sense that if X and Y are independent random variables then

μ₁(X + Y) = μ₁(X) + μ₁(Y)

and

and

μ₃(X + Y) = μ₃(X) + μ₃(Y)

(independence is not needed for the first of these three identities; for the second it can be weakened to uncorrelatedness).

The central moments beyond the third lack this linearity; in that respect they differ from the cumulants (the first three cumulants are the same as the first moment and the second and third central moments; the higher cumulants have a more complicated relationship with the central moments).

Like the cumulants, the factorial moments of a probability distribution are also polynomial functions of the moments.

See also

• Standardized moment
• Moment about the mean

External links

Mathworld Website