The first family of Mittag-Leffler distributions is defined by a relation between the Mittag-Leffler function and their
cumulative distribution functions. For all \alpha \in (0, 1], the function E_\alpha is increasing on the real line, converges to 0 in - \infty, and E_\alpha (0) = 1. Hence, the function x \mapsto 1-E_\alpha (-x^\alpha) is the cumulative distribution function of a probability measure on the non-negative real numbers. The distribution thus defined, and any of its multiples, is called a Mittag-Leffler distribution of order \alpha. All these probability distributions are
absolutely continuous. Since E_1 is the exponential function, the Mittag-Leffler distribution of order 1 is an
exponential distribution. However, for \alpha \in (0, 1), the Mittag-Leffler distributions are
heavy-tailed, with :E_\alpha (-x^\alpha) \sim \frac{x^{-\alpha}}{\Gamma(1-\alpha)}, \quad x \to \infty. Their Laplace transform is given by: :\mathbb{E} (e^{- \lambda X_\alpha}) = \frac{1}{1+\lambda^\alpha}, which implies that, for \alpha \in (0, 1), the expectation is infinite. In addition, these distributions are
geometric stable distributions. Parameter estimation procedures can be found here. ==Second family of Mittag-Leffler distributions==