伽玛分布

• Gamma分配的矩母函数（m.g.f）

${\displaystyle M_{x}\left(t\right)=E\left(e^{xt}\right)={\frac {\lambda ^{\alpha }}{\Gamma \left(\alpha \right)}}\int _{0}^{\infty }e^{xt}x^{\alpha -1}e^{-\lambda x}dx=\left({\frac {\lambda }{\lambda -t}}\right)^{\alpha }=\left(1-{\beta }{t}\right)^{-\alpha }}$

• 概率母函数（p.g.f）

${\displaystyle K_{x}\left(t\right)=\ln M_{x}\left(t\right)=\alpha \left[\ln \lambda -\ln \left(\lambda -t\right)\right]}$

• 期望值

${\displaystyle {\frac {dK_{x}\left(t\right)}{dt}}={\frac {\alpha }{\lambda -t}},\quad when(t=0),E\left(X\right)={\frac {\alpha }{\color {Red}\lambda }}=\alpha {\color {Red}\beta }}$

• 方差

${\displaystyle {\frac {d^{2}K_{x}\left(t\right)}{dt^{2}}}={\frac {\alpha }{\left(\lambda -t\right)^{2}}},\quad when(t=0),\sigma ^{2}\left(X\right)={\frac {\alpha }{\color {Red}{\lambda ^{2}}}}=\alpha {\color {Red}{\beta ^{2}}}}$

當兩隨機變數服從Gamma分布，且相互獨立，且母數（${\displaystyle \lambda }$或${\displaystyle \beta }$）相同時，Gamma分布具有可加性。

${\displaystyle \coprod {\begin{cases}r.v.X\sim \Gamma \left(\alpha _{1},{\color {green}\lambda }\right)\\r.v.Y\sim \Gamma \left(\alpha _{2},{\color {green}\lambda }\right)\end{cases}}\Longrightarrow X+Y\sim \Gamma \left({\color {green}\alpha _{1}+\alpha _{2}},\lambda \right)}$