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Difference between revisions of "Erlang distribution"

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The probability distribution concentrated on $(0,\infty)$ with density
 
The probability distribution concentrated on $(0,\infty)$ with density
  
 
$$p(x)=\frac{(n\mu)^n}{\Gamma(n)}x^{n-1}e^{-n\mu x},\quad x>0,$$
 
$$p(x)=\frac{(n\mu)^n}{\Gamma(n)}x^{n-1}e^{-n\mu x},\quad x>0,$$
  
where the integer $n\geq1$ and the real number $\mu>0$ are parameters. The characteristic function of the Erlang distribution has the form
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where the integer $n\geq1$ and the real number $\mu>0$ are parameters. The [[characteristic function]] of the Erlang distribution has the form
  
 
$$\left(1-\frac{it}{n\mu}\right)^{-n},$$
 
$$\left(1-\frac{it}{n\mu}\right)^{-n},$$
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and the mathematical expectation and variance are $1/\mu$ and $1/n\mu^2$, respectively.
 
and the mathematical expectation and variance are $1/\mu$ and $1/n\mu^2$, respectively.
  
The Erlang distribution is special case of the [[Gamma-distribution|gamma-distribution]]: $p(x)=\alpha g_\lambda(\alpha x)$, where $g_\lambda(y)$ is the density of the gamma-distribution for $\lambda=n$, and where $\alpha=n\mu$. For $n=1$ the Erlang distribution is the same as the [[Exponential distribution|exponential distribution]] with parameter $\mu$. The Erlang distribution with parameters $n$ and $\mu$ can be found as the distribution of the sum of $n$ independent random variables having the same exponential distribution with parameter $n\mu$. As $n\to\infty$, the Erlang distribution tends to the [[Degenerate distribution|degenerate distribution]] concentrated at the point $1/\mu$.
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The Erlang distribution is a special case of the [[gamma-distribution]]: $p(x)=\alpha g_\lambda(\alpha x)$, where $g_\lambda(y)$ is the density of the gamma-distribution for $\lambda=n$, and where $\alpha=n\mu$. For $n=1$ the Erlang distribution is the same as the [[exponential distribution]] with parameter $\mu$. The Erlang distribution with parameters $n$ and $\mu$ can be found as the distribution of the sum of $n$ independent random variables having the same exponential distribution with parameter $n\mu$. As $n\to\infty$, the Erlang distribution tends to the [[degenerate distribution]] concentrated at the point $1/\mu$.
  
The selection of the Erlang distribution from the system of gamma-distributions is explained by its use in queueing theory. In many random queueing processes the Erlang distribution appears as the distribution of intervals among random events or as the distribution of the queueing time. Sometimes the Erlang distribution is defined as the gamma-distribution with the density
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The selection of the Erlang distribution from the system of gamma-distributions is explained by its use in [[queueing theory]]. In many random queueing processes the Erlang distribution appears as the distribution of intervals among random events or as the distribution of the queueing time. Sometimes the Erlang distribution is defined as the gamma-distribution with the density
  
 
$$\frac{\alpha^n}{\Gamma(n)}x^{n-1}e^{-\alpha x},\quad x>0.$$
 
$$\frac{\alpha^n}{\Gamma(n)}x^{n-1}e^{-\alpha x},\quad x>0.$$
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====References====
 
====References====
<table><TR><TD valign="top">[1]</TD> <TD valign="top">  T.L. Saaty,  "On elements of queueing theory with applications" , McGraw-Hill  (1961)</TD></TR></table>
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<table>
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<TR><TD valign="top">[1]</TD> <TD valign="top">  T.L. Saaty,  "On elements of queueing theory with applications" , McGraw-Hill  (1961)</TD></TR>
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</table>

Latest revision as of 19:37, 4 December 2016

2020 Mathematics Subject Classification: Primary: 60E05 Secondary: 60K25 [MSN][ZBL]

The probability distribution concentrated on $(0,\infty)$ with density

$$p(x)=\frac{(n\mu)^n}{\Gamma(n)}x^{n-1}e^{-n\mu x},\quad x>0,$$

where the integer $n\geq1$ and the real number $\mu>0$ are parameters. The characteristic function of the Erlang distribution has the form

$$\left(1-\frac{it}{n\mu}\right)^{-n},$$

and the mathematical expectation and variance are $1/\mu$ and $1/n\mu^2$, respectively.

The Erlang distribution is a special case of the gamma-distribution: $p(x)=\alpha g_\lambda(\alpha x)$, where $g_\lambda(y)$ is the density of the gamma-distribution for $\lambda=n$, and where $\alpha=n\mu$. For $n=1$ the Erlang distribution is the same as the exponential distribution with parameter $\mu$. The Erlang distribution with parameters $n$ and $\mu$ can be found as the distribution of the sum of $n$ independent random variables having the same exponential distribution with parameter $n\mu$. As $n\to\infty$, the Erlang distribution tends to the degenerate distribution concentrated at the point $1/\mu$.

The selection of the Erlang distribution from the system of gamma-distributions is explained by its use in queueing theory. In many random queueing processes the Erlang distribution appears as the distribution of intervals among random events or as the distribution of the queueing time. Sometimes the Erlang distribution is defined as the gamma-distribution with the density

$$\frac{\alpha^n}{\Gamma(n)}x^{n-1}e^{-\alpha x},\quad x>0.$$

It is named for A. Erlang, who was the first to construct mathematical models in queueing problems.

References

[1] T.L. Saaty, "On elements of queueing theory with applications" , McGraw-Hill (1961)
How to Cite This Entry:
Erlang distribution. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Erlang_distribution&oldid=33169
This article was adapted from an original article by A.V. Prokhorov (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article