We assume that, for all non-zero natural integer $n$, $X$ admits a moment of order $n$ and that the power series $\sum _ { n \geqslant 0 } m _ { n } ( X ) \frac { t ^ { n } } { n ! }$ has a radius of convergence $R _ { X } > 0$. For all $t \in ] - R _ { X } , R _ { X } [$, we denote $M _ { X } ( t ) = \sum _ { n = 0 } ^ { + \infty } m _ { n } ( X ) \frac { t ^ { n } } { n ! }$. Show conversely that, if there exists a real $R > 0$ such that, for all $t \in ] - R , R [$, the random variable $\mathrm { e } ^ { t X }$ admits an expectation, then the domain of definition of the moment generating function of $X$ contains $] - R , R [$ and for all $t \in ] - R , R \left[ , M _ { X } ( t ) = \mathbb { E } \left( \mathrm { e } ^ { t X } \right) \right.$.
We assume that, for all non-zero natural integer $n$, $X$ admits a moment of order $n$ and that the power series $\sum _ { n \geqslant 0 } m _ { n } ( X ) \frac { t ^ { n } } { n ! }$ has a radius of convergence $R _ { X } > 0$. For all $t \in ] - R _ { X } , R _ { X } [$, we denote $M _ { X } ( t ) = \sum _ { n = 0 } ^ { + \infty } m _ { n } ( X ) \frac { t ^ { n } } { n ! }$.
Show conversely that, if there exists a real $R > 0$ such that, for all $t \in ] - R , R [$, the random variable $\mathrm { e } ^ { t X }$ admits an expectation, then the domain of definition of the moment generating function of $X$ contains $] - R , R [$ and for all $t \in ] - R , R \left[ , M _ { X } ( t ) = \mathbb { E } \left( \mathrm { e } ^ { t X } \right) \right.$.