Erlang distribution

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Erlang
Probability density function
Cumulative distribution function
Parameters	$k > 0\,$ shape (integer) $\lambda > 0\,$ rate (real) alt.: $\theta = 1/\lambda > 0\,$ scale (real)
Support	$x \in [0; \infty)\!$
Probability density function (pdf)	$\frac{\lambda^k x^{k-1} e^{-\lambda x}}{(k-1)!\,}$
Cumulative distribution function (cdf)	$\frac{\gamma(k, \lambda x)}{(k-1)!}=1-\sum_{n=0}^{k-1}e^{-\lambda x}(\lambda x)^{n}/n!$
Mean	$k/\lambda\,$
Median	no simple closed form
Mode	$(k-1)/\lambda\,$ for $k \geq 1\,$
Variance	$k /\lambda^2\,$
Skewness	$\frac{2}{\sqrt{k}}$
Excess kurtosis	$\frac{6}{k}$
Entropy	$k/\lambda+(k-1)\ln(\lambda)+\ln((k-1)!)\,$ $+(1-k)\psi(k)\,$
Moment-generating function (mgf)	$(1 - t/\lambda)^{-k}\,$ for $t < \lambda\,$
Characteristic function	$(1 - it/\lambda)^{-k}\,$

The Erlang distribution is a continuous probability distribution with wide applicability primarily due to its relation to the exponential and Gamma distributions. The Erlang distribution was developed by A. K. Erlang to examine the number of telephone calls which might be made at the same time to the operators of the switching stations. This work on telephone traffic engineering has been expanded to consider waiting times in queueing systems in general. The distribution is now used in the field of stochastic processes.

1 Overview
2 Characterization
- 2.1 Probability density function
- 2.2 Cumulative distribution function
3 Occurrence
4 See also
5 External links

[edit] Overview

The distribution is a continuous distribution, which has a positive value for all real numbers greater than zero, and is given by two parameters: the shape $k$ , which is an integer, and the rate $λ$ , which is a real. The distribution is sometimes defined using the inverse of the rate parameter, the scale $θ$ .

When the shape parameter $k$ equals 1, the distribution simplifies to the exponential distribution.

The Erlang distribution is a special case of the Gamma distribution where the shape parameter $k$ is an integer. In the Gamma distribution, this parameter is a real.

[edit] Characterization

[edit] Probability density function

The probability density function of the Erlang distribution is

$f(x; k,\lambda)={\lambda^k x^{k-1} e^{-\lambda x} \over (k-1)!}\quad\mbox{for }x>0.$

where e is the base of the natural logarithm and $!$ is the factorial function. The parameter $k$ is called the shape parameter and the parameter $λ$ is called the rate parameter. An alternative, but equivalent, parametrization uses the scale parameter $θ$ which is simply the inverse of the rate parameter (i.e. $θ = 1 / λ$ ):

$f(x; k,\theta)=\frac{ x^{k-1} e^{-\frac{x}{\theta}} }{\theta^k(k-1)!}\quad\mbox{for }x>0.$

Because of the factorial function in the denominator, the Erlang distribution is only defined when the parameter k is a positive integer. The Gamma distribution generalizes the Erlang by allowing its first parameter to be a real, using the gamma function instead of the factorial function.

[edit] Cumulative distribution function

The cumulative distribution function of the Erlang distribution is

$F(x; k,\lambda) = \frac{\gamma(k, \lambda x)}{(k-1)!}$

where $γ()$ is the lower incomplete gamma function. The CDF may also be expressed as

$F(x; k,\lambda) = 1-\sum_{n=0}^{k-1}e^{-\lambda x}(\lambda x)^{n}/n!$

[edit] Occurrence

[edit] Waiting times

Events which occur independently with some average rate are modeled with a Poisson process. The waiting times between k occurrences of the event are Erlang distributed. (The related question of the number of events in a given amount of time is described by the Poisson distribution.)

The Erlang distribution, which measures the time between incoming calls, can be used in conjunction with the expected duration of incoming calls to produce information about the traffic load measured in Erlang units. This can be used to determine the probability of packet loss or delay, according to various assumptions made about whether blocked calls are aborted (Erlang B formula) or queued until served (Erlang C formula). The Erlang B and C formulas are still in everyday use for traffic modelling for applications such as the design of call centers.

[edit] Compartment models

The Erlang distribution also occurs as a description of the rate of transition of elements through a system of compartments. Such systems are widely used in biology and ecology.

[edit] Stochastic processes

The Erlang distribution is the distribution of the sum of k independent identically distributed random variables each having an exponential distribution.

[edit] See also

	Probability distributions [ view • talk • edit ]
	Univariate	Multivariate
Discrete:	Benford • Bernoulli • binomial • Boltzmann • categorical • compound Poisson • degenerate • Gauss-Kuzmin • geometric • hypergeometric • logarithmic • negative binomial • parabolic fractal • Poisson • Rademacher • Skellam • uniform • Yule-Simon • zeta • Zipf • Zipf-Mandelbrot	Ewens • multinomial • multivariate Polya
Continuous:	Beta • Beta prime • Cauchy • chi-square • Dirac delta function • Erlang • exponential • exponential power • F • fading • Fisher's z • Fisher-Tippett • Gamma • generalized extreme value • generalized hyperbolic • generalized inverse Gaussian • Half-Logistic • Hotelling's T-square • hyperbolic secant • hyper-exponential • hypoexponential • inverse chi-square (scaled inverse chi-square)• inverse Gaussian • inverse gamma (scaled inverse gamma) • Kumaraswamy • Landau • Laplace • Lévy • Lévy skew alpha-stable • logistic • log-normal • Maxwell-Boltzmann • Maxwell speed • normal (Gaussian) • normal inverse Gaussian • Pareto • Pearson • polar • raised cosine • Rayleigh • relativistic Breit-Wigner • Rice • shifted Gompertz • Student's t • triangular • type-1 Gumbel • type-2 Gumbel • uniform • Variance-Gamma • Voigt • von Mises • Weibull • Wigner semicircle • Wilks' lambda	Dirichlet • inverse-Wishart • Kent • matrix normal • multivariate normal • multivariate Student • von Mises-Fisher • Wigner quasi • Wishart
Miscellaneous:	Cantor • conditional • exponential family • infinitely divisible • location-scale family • marginal • maximum entropy • phase-type • posterior • prior • quasi • sampling • singular