Search results
Results from the WOW.Com Content Network
The characteristic function of a real-valued random variable always exists, since it is an integral of a bounded continuous function over a space whose measure is finite. A characteristic function is uniformly continuous on the entire space. It is non-vanishing in a region around zero: φ(0) = 1. It is bounded: | φ(t) | ≤ 1.
The characteristic function of a cooperative game in game theory. The characteristic polynomial in linear algebra. The characteristic state function in statistical mechanics. The Euler characteristic, a topological invariant. The receiver operating characteristic in statistical decision theory. The point characteristic function in statistics.
Here are some examples of the moment-generating function and the characteristic function for comparison. It can be seen that the characteristic function is a Wick rotation of the moment-generating function M X ( t ) {\displaystyle M_{X}(t)} when the latter exists.
If the characteristic function of some random variable is of the form () = in a neighborhood of zero, where () is a polynomial, then the Marcinkiewicz theorem (named after Józef Marcinkiewicz) asserts that can be at most a quadratic polynomial, and therefore is a normal random variable. [33]
In classical mathematics, characteristic functions of sets only take values 1 (members) or 0 (non-members). In fuzzy set theory, characteristic functions are generalized to take value in the real unit interval [0, 1], or more generally, in some algebra or structure (usually required to be at least a poset or lattice).
Lévy’s continuity theorem: The sequence {X n} converges in distribution to X if and only if the sequence of corresponding characteristic functions {φ n} converges pointwise to the characteristic function φ of X. Convergence in distribution is metrizable by the Lévy–Prokhorov metric.
For example, the log-normal function with such fits well with the size of secondarily produced droplets during droplet impact [56] and the spreading of an epidemic disease. [ 57 ] The value σ = 1 / 6 {\textstyle \sigma =1{\big /}{\sqrt {6}}} is used to provide a probabilistic solution for the Drake equation.
Julia provides package StableDistributions.jl which has methods of generation, fitting, probability density, cumulative distribution function, characteristic and moment generating functions, quantile and related functions, convolution and affine transformations of stable distributions. It uses modernised algorithms improved by John P. Nolan.