# Mittag-Leffler Function and Probability Distribution

# Mittag-Leffler Function and Probability Distribution

### Okay, this article isn't for everyone. But if you do work with sophisticated probability distributions in your scientific and mathematical programming, then you will find this interesting. For all others: "move along these are not the distributions you are looking for."

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The Mittag-Leffler function is a generalization of the exponential function. Since *k*!= Γ(*k* + 1), we can write the exponential function’s power series as

and we can generalize this to the Mittag-Leffler function

which reduces to the exponential function when α = β = 1. There are a few other values of α and β for which the Mittag-Leffler function reduces to more familiar functions. For example,

and

where erfc(*x*) is the complementary error function.

## History

Mittag-Leffler was one person, not two. When I first saw the Mittag-Leffler theorem in complex analysis, I assumed it was named after two people, Mittag and Leffler. But the theorem and the function discussed here are named after one man, the Swedish mathematician Magnus Gustaf (Gösta) Mittag-Leffler (1846–1927).

The function that Mr. Mittag-Leffler originally introduced did not have a β parameter; that generalization came later. The function *E*_{α} is *E*_{α, 1}.

## Mittag-Leffler Probability Distributions

Just as you can make a couple probability distributions out of the exponential function, you can make a couple probability distributions out of the Mittag-Leffler function.

### Continuous Mittag-Leffler Distribution

The exponential function exp(-*x*) is positive over [0, ∞) and integrates to 1, so we can define a probability distribution whose density (PDF) function is *f*(*x*) = exp(-*x*) and whose distribution function (CDF) is *F*(*x*) = 1 – exp(-*x*). The Mittag-Leffler distribution has CDF is 1 – *E*_{α}(-*x*^{α}) and so reduces to the exponential distribution when α = 1. For 0 < α < 1, the Mittag-Leffler distribution is a heavy-tailed generalization of the exponential. [1]

### Discrete Mittag-Leffler Distribution

The Poisson distribution comes from taking the power series for exp(λ), normalizing it to 1, and using the *k*th term as the probability mass for *k*. That is,

The analogous discrete Mittag-Leffler distribution [2] has probability mass function

## Fractional Differential Equations

In addition to probability and statistics, the Mittag-Leffler function comes up in fractional calculus. It plays a role analogous to that of the exponential distribution in classical calculus. Just as the solution to the simply differential equation

is exp(*ax*), for 0 < μ < 1, the soltuion to the **fractional differential equation**

is *ax*^{μ-1}*E*_{μ, μ}(*ax*^{μ}). Note that this reduces to exp(*ax*) when μ = 1. [3]

## References

[1] Gwo Dong Lin. Journal of Statistical Planning and Inference 74 (1998) 1–9, On the Mittag–Leffler distributions

[2] Subrata Chakraborty, S. H. Ong. Mittag-Leffler function distribution: A new generalization of hyper-Poisson distribution. arXiv:1411.0980v1

[3] Keith Oldham, Jan Myland, Jerome Spanier. An Atlas of Functions. Springer.

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