Inversion or recovery of distribution from generating/characteristic function

The question asks to recover probabilities P(X = k) from the generating function or characteristic function using inversion formulas, integral representations, or limit arguments.

grandes-ecoles 2015 QIV.E View
We assume that, for all $k\in\mathbb{N}$, $p_k=\frac{1}{2^{k+1}}$. We have $\varphi_n(t)=\frac{n+(1-n)t}{1+n-nt}$.
Express, for $(n,k)\in\mathbb{N}^2$, $P(Y_n=k)$ in terms of $n$ and $k$.
grandes-ecoles 2020 Q20 View
Let $X$ be a real and discrete random variable and $m \in \mathbb { R }$. For $T \in \mathbb { R } _ { + } ^ { * }$, we set $V _ { m } ( T ) = \frac { 1 } { 2 T } \int _ { - T } ^ { T } \phi _ { X } ( t ) \mathrm { e } ^ { - \mathrm { i } m t } \mathrm {~d} t$. We assume that $X ( \Omega )$ is countable and we use the notations of question 2: $X ( \Omega ) = \left\{ x _ { n } , n \in \mathbb { N } \right\}$ with $a _ { n } = \mathbb { P } \left( X = x _ { n } \right)$. Using the results of Q17--Q19, establish that $V _ { m } ( T ) \xrightarrow [ T \rightarrow + \infty ] { } \mathbb { P } ( X = m )$.