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If X is a r.v. such that E(X^n)=n! Find the m.g.f. of X,Mx(t). Also find the ch.f. of X,and from this deduce the distribution of X.

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M_X(t)=\mathbb E(e^(Xt))

M_X(t)=\mathbb E\left(1+Xt+(t^2)/(2!)X^2+(t^3)/(3!)X^3+\cdots\right)

M_X(t)=\mathbb E(1)+t\mathbb E(X)+(t^2)/(2!)\mathbb E(X^2)+(t^3)/(3!)\mathbb E(X^3)+\cdots

M_X(t)=1+t+t^2+t^3+\cdots

M_X(t)=\displaystyle\sum_(k\ge0)t^k=\frac1{1-t}

provided that
|t|<1.

Similarly,


\varphi_X(t)=\mathbb E(e^(iXt))

\varphi_X(t)=1+it+(it)^2+(it)^3+\cdots

\varphi_X(t)=(1-t^2+t^4-t^6+\cdots)+it(1-t^2+t^4-t^6+\cdots)

\varphi_X(t)=(1+it)(1-t^2+t^4-t^6+\cdots)

\varphi_X(t)=(1+it)/(1+t^2)=\frac1{1-it}

You can find the CDF/PDF using any of the various inversion formulas. One way would be to compute


F_X(x)=\displaystyle\frac12+\frac1{2\pi}\int_0^\infty(e^(itx)\varphi_X(-t)-e^(-itx)\varphi_X(t))/(it)\,\mathrm dt

The integral can be rewritten as


\displaystyle\int_0^\infty(2i\sin(tx)-2it\cos(tx))/(it(1+t^2))\,\mathrm dt

so that


F_X(x)=\displaystyle\frac12+\frac1{2\pi}\int_0^\infty(\sin(tx)-t\cos(tx))/(t(1+t^2))\,\mathrm dt

There are lots of ways to compute this integral. For instance, you can take the Laplace transform with respect to
x, which gives


\displaystyle\mathcal L_s\left\{\int_0^\infty(\sin(tx)-t\cos(tx))/(t(1+t^2))\,\mathrm dt\right\}=\int_0^\infty(1-s)/((1+t^2)(s^2+t^2))\,\mathrm dt

=\displaystyle(\pi(1-s))/(2s(1+s))

and taking the inverse transform returns


F_X(x)=\frac12+\frac1\pi\left(\frac\pi2-\pi e^(-x)\right)=1-e^(-x)

which describes an exponential distribution with parameter
\lambda=1.
User Chris Franklin
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