Moment (mathematics)

In mathematics, the moments of a function are certain quantitative measures related to the shape of the function's graph. If the function represents mass density, then the zeroth moment is the total mass, the first moment (normalized by total mass) is the center of mass, and the second moment is the moment of inertia. If the function is a probability distribution, then the first moment is the expected value, the second central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis. The mathematical concept is closely related to the concept of moment in physics. For a distribution of mass or probability on a bounded interval, the collection of all the moments (of all orders, from 0 to ∞) uniquely determines the distribution (Hausdorff moment problem). The same is not true on unbounded intervals (Hamburger moment problem). In the mid-nineteenth century, Pafnuty Chebyshev became the first person to think systematically in terms of the moments of random variables. The n-th raw moment (i.e., moment about zero) of a distribution is defined bywhereThe n-th moment of a real-valued continuous function f(x) of a real variable about a value c is the integralIt is possible to define moments for random variables in a more general fashion than moments for real-valued functions — see moments in metric spaces. The moment of a function, without further explanation, usually refers to the above expression with c = 0. For the second and higher moments, the central moment (moments about the mean, with c being the mean) are usually used rather than the moments about zero, because they provide clearer information about the distribution's shape. Other moments may also be defined. For example, the nth inverse moment about zero is and the n-th logarithmic moment about zero is The n-th moment about zero of a probability density function f(x) is the expected value of X^n and is called a raw moment or crude moment.

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

Extensions of Peer Prediction Incentive Mechanisms

Higher-order moments of the elliptic flow distribution in PbPb collisions at √sNN=5.02 TeV

From trees to barcodes and back again II: Combinatorial and probabilistic aspects of a topological inverse problem

Extensions of Peer Prediction Incentive Mechanisms

Higher-order moments of the elliptic flow distribution in PbPb collisions at √sNN=5.02 TeV