Moment-Generating Function

A Moment-Generating Function is a generating function a real valued random variable X with distribution [math]\displaystyle{ F(x) = P(X ≤ x) }[/math] is defined by [math]\displaystyle{ M_X(t) = E[e^{tX}] = ∫e^{tx}dF(x) }[/math].

AKA: MGF.
- …
Counter-Example(s):
- a Probability-Generating Function.
See: Moment, Discrete Random Variable, Statistic, Cumulative Distribution Function, Characteristic Function (Probability Theory).

References

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Moment-generating_function Retrieved:2014-10-17.
- In probability theory and statistics, the moment-generating function of a random variable is an alternative specification of its probability distribution. Thus, it provides the basis of an alternative route to analytical results compared with working directly with probability density functions or cumulative distribution functions. There are particularly simple results for the moment-generating functions of distributions defined by the weighted sums of random variables. Note, however, that not all random variables have moment-generating functions.
  In addition to univariate distributions, moment-generating functions can be defined for vector- or matrix-valued random variables, and can even be extended to more general cases.
  The moment-generating function does not always exist even for real-valued arguments, unlike the characteristic function. There are relations between the behavior of the moment-generating function of a distribution and properties of the distribution, such as the existence of moments.

Beran, Jan, and Sucharita Ghosh. “Moment Generating Function." In International Encyclopedia of Statistical Science, pp. 852-854. Springer Berlin Heidelberg, 2011.
- http://link.springer.com/referenceworkentry/10.1007%2F978-3-642-04898-2_375
- QUOTE: The moment generating function (mgf) of a real valued random variable X with distribution F(x) = P(X ≤ x) is defined by [math]\displaystyle{ M_X(t)=E[e^{tX}]=∫e^{tx}dF(x). \ (1) }[/math] For distributions with a density function f = F′, M_X can also be interpreted as a (two-sided) Laplace transform of f.

(Dubnicka, 2006d) ⇒ Suzanne R. Dubnicka. (2006). “Moment-Generating Functions - Handout 4." Kansas State University, Introduction to Probability and Statistics I, STAT 510 - Fall 2006.
- TERMINOLOGY : Let X be a discrete random variable with pmf pX(x) and support SX. The moment generating function (mgf) for X, denoted by MX(t), is given by MX(t) = E(etX) = X x∈SX etxpX(x),

(Davison, 2003) ⇒ Anthony C. Davison. (2003). “Statistical Models." Cambridge University Press. ISBN:0521773393