Parameters Probability density function Cumulative distribution function $-\infty $n\geq 1$ integer $x\in [a,b]$ see below ${\tfrac {1}{2))(a+b)$ ${\tfrac {1}{12n))(b-a)^{2)$ 0 $-{\tfrac {6}{5n))$ $\left(-{\frac {in(e^{\tfrac {ibt}{n))-e^{\tfrac {iat}{n)))}{(b-a)t))\right)^{n)$ In probability and business statistics, the Bates distribution, named after Grace Bates, is a probability distribution of the mean of a number of statistically independent uniformly distributed random variables on the unit interval. This distribution is related to the uniform, the triangular, and the normal Gaussian distribution, and has applications in broadcast engineering for signal enhancement. The Bates distribution is sometimes confused with the Irwin–Hall distribution, which is the distribution of the sum (not the mean) of n independent random variables uniformly distributed from 0 to 1. Thus, the two distributions are simply versions of each other as they only differ in scale.

## Definition

The Bates distribution is the continuous probability distribution of the mean, X, of n independent, uniformly distributed, random variables on the unit interval, Uk:

$X={\frac {1}{n))\sum _{k=1}^{n}U_{k}.$ The equation defining the probability density function of a Bates distribution random variable X is

$f_{X}(x;n)={\frac {n}{2(n-1)!))\sum _{k=0}^{n}(-1)^{k}{n \choose k}(nx-k)^{n-1}\operatorname {sgn}(nx-k)$ for x in the interval (0,1), and zero elsewhere. Here sgn(nxk) denotes the sign function:

$\operatorname {sgn}(nx-k)={\begin{cases}-1&nxk.\end{cases))$ More generally, the mean of n independent uniformly distributed random variables on the interval [a,b]

$X_{(a,b)}={\frac {1}{n))\sum _{k=1}^{n}U_{k}(a,b).$ would have the probability density function (PDF) of

$g(x;n,a,b)={\frac {1}{b-a))f_{X}\left({\frac {x-a}{b-a));n\right){\text{ for ))a\leq x\leq b$ ## Extensions and Applications

This section needs expansion. You can help by adding to it. (February 2020)

With a few modifications, the Bates distribution encompasses the uniform, the triangular and, taking the limit as n goes to infinity, also the normal Gaussian distribution.

Replacing the term ${\frac {1}{n))$ when calculating the mean, X, with ${\frac {1}{\sqrt {n)))$ will create a similar distribution with a constant variance, such as unity. Then, by subtracting the mean, the resulting mean of the distribution will be set at zero. Thus the parameter n would become a purely shape-adjusting parameter. By also allowing n to be a non-integer, a highly flexible distribution can be created, for example, U(0,1) + 0.5U(0,1) gives a trapezoidal distribution.

The Student-t distribution provides a natural extension of the normal Gaussian distribution for modeling of long tail data. A Bates distribution that has been generalized as previously stated fulfills the same purpose for short tail data.

The Bates distribution has an application to beamforming and pattern synthesis in the field of electrical engineering. The distribution was found to increase the beamwidth of the main lobe, representing an increase in the signal of the radiation pattern in a single direction, while simultaneously reducing the, usually undesirable, sidelobe levels. [page needed]