Combinant

In the mathematical theory of probability, the combinants cn of a random variable X are defined via the combinant-generating function G(t), which is defined from the moment generating function M(z) as


 * $$G_X(t)=M_X(\log(1+t))$$

which can be expressed directly in terms of a random variable X as


 * $$ G_X(t) := E\left[(1+t)^X\right], \quad t \in \mathbb{R}, $$

wherever this expectation exists.

The nth combinant can be obtained as the nth derivatives of the logarithm of combinant generating function evaluated at –1 divided by n factorial:


 * $$ c_n = \frac{1}{n!} \frac{\partial ^n}{\partial t^n} \log(G (t)) \bigg|_{t=-1} $$

Important features in common with the cumulants are:
 * the combinants share the additivity property of the cumulants;
 * for infinite divisibility (probability) distributions, both sets of moments are strictly positive.