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Probability density function 

Cumulative distribution function 

Parameters  shape , rate (real) alt.: scale (real) 

Support  
CDF  
Mean  
Median  No simple closed form 
Mode  for 
Variance  
Skewness  
Ex. kurtosis  
Entropy  
MGF  for 
CF 
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 fields of stochastic processes and of biomathematics.
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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 , which is a nonnegative integer, and the rate , which is a nonnegative real number. The distribution is sometimes defined using the inverse of the rate parameter, the scale . It is the distribution of the sum of independent exponential variables with mean .
When the shape parameter equals 1, the distribution simplifies to the exponential distribution. The Erlang distribution is a special case of the Gamma distribution where the shape parameter is an integer. In the Gamma distribution, this parameter is not restricted to the integers.
The probability density function of the Erlang distribution is
The parameter is called the shape parameter and the parameter is called the rate parameter. An alternative, but equivalent, parametrization uses the scale parameter which is the reciprocal of the rate parameter (i.e., ):
When the scale parameter equals 2, then distribution simplifies to the chisquared distribution with 2k degrees of freedom. It can therefore be regarded as a generalized chisquared distribution.
Because of the factorial function in the denominator, the Erlang distribution is only defined when the parameter k is a positive integer. In fact, this distribution is sometimes called the Erlangk distribution (e.g., an Erlang2 distribution is an Erlang distribution with k=2). The Gamma distribution generalizes the Erlang by allowing to be any real number, using the gamma function instead of the factorial function.
The cumulative distribution function of the Erlang distribution is:
where is the lower incomplete gamma function. The CDF may also be expressed as
Events that 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 ErlangB and C formulae are still in everyday use for traffic modeling for applications such as the design of call centers.
A.K. Erlang worked a lot in traffic modeling. There are thus two other Erlang distributions, both used in modeling traffic:
Erlang B distribution: this is the easier of the two, and can be used, for example, in a call centre to calculate the number of trunks one need to carry a certain amount of phone traffic with a certain "target service".
Erlang C distribution: this formula is much more difficult and is often used, for example, to calculate how long callers will have to wait before being connected to a human in a call centre or similar situation.
The Erlang distribution is the distribution of the sum of k independent identically distributed random variables each having an exponential distribution. The longrun rate at which events occur is the reciprocal of the expectation of , that is . The (age specific event) rate of the Erlang distribution is, for , monotonic in , increasing from zero at , to as tends to infinity.^{[1]}

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