음이항분포
- r번 성공할 때 까지 독립적으로 실시한 베르누이 시행의 총 횟수
\begin{align*}
X &\sim negative\;binominal(r, p) \\[20pt]
P(X=x) &= \binom{x-1}{r-1}~
p^{r-1}~(1-p)^{x-r}~p~~
I(x \in \{r, r+1, ...\})
\\[20pt]
E(x) &= \frac{r}{p}
\\[20pt]
Var(X) &= \frac{r(1-p)}{p^2}
\end{align*}
확률함수 조건 확인
- P(X = x) ≥ 0
\binom{x-1}{r-1} \ge 0, ~~
p^{r}(1-p)^{x-r} \ge 0, ~~
I(x \in \{r, r+1, ...\}) \ge 0
- \textstyle\sum P(X = x) = 1
- 여기서는 테일러 급수를 사용한다.
- 테일러 급수를 어떻게 사용했는지 보려면 클릭!
\begin{align*}
\sum_{x}~P(X=x)
&= ~\sum_{x}
\binom{x-1}{r-1}~
p^{r}~(1-p)^{x-r}
\\[20pt]
&= p^r~\sum_{x=0}^{\infty}
\binom{r+x-1}{r-1}~
(1-p)^{k}
\\[20pt]
&= p^r~
\{1-(1-p)^r\}
\\[20pt]
&= p^r \cdot p^{-r} = 1
\end{align*}
\therefore~\sum~P(X=x) = 1
1차 적률
E(X) = \dfrac{r}{p}
\begin{align*}
E(X) &= \sum_{x}~x~P(X=x)
\\[20pt]
&= \sum_{x=r}^{\infty}~x\cdot
\binom{x-1}{r-1}~p^r(1-p)^{x-r}
\\[20pt]
&= \sum_{x=r}^{\infty}~
\dfrac
{x\cdot(x-1)!}
{(r-1)!(x-r)!}~
p^r(1-p)^{x-r}
\\[20pt]
&= \sum_{x=r}^{\infty}~
\dfrac
{x! \cdot r}
{(r-1)!(x-r)!\cdot r}
~~p^r(1-p)^{x-r}
\\[20pt]
&= \sum_{x=r}^{\infty}~
r~\binom{x}{r}~p^r(1-p)^{x-r}
\\[20pt]
&= r~\sum_{x=r}^{\infty}
\binom{x}{r}~p^r(1-p)^{x-r}
\\[20pt]
&=~rp^r \cdot
\sum_{x=r}^{\infty}
\binom{r+k}{r}
(1-p)^k
\\[20pt]
&=~rp^r \cdot
\sum_{x=r}^{\infty}
\binom
{(r+1)-1-k}
{(r+1)-1}
(1-p)^k
\\[20pt]
&=~rp^r \cdot
\sum_{x=r}^{\infty}
\binom
{r_*-1+k}
{r_*-1}
(1-p)^k
\\[20pt]
&= rp^r \cdot
\{1-(1-p)\}^{-r_n}
= rp^r \cdot
\{1-(1-p)\}^{-r+1}
\\[20pt]
&= rp^r\cdot\dfrac{1}{p^{r+1}}
= \dfrac{r}{p}
\end{align*}
2차 적률와 분산
- 2차 적률
\begin{align*}
E(X^2) &= E(X(X-1)) - E(X)
\\[10pt]
&= \dfrac{r(r+1)}{p^2} - \dfrac{r}{p}
\end{align*}
- E(X(X-1))
\begin{align*}
E(X(X-1)) &=~
\sum_{x=r}^{\infty}
~x(x+1)~\binom{x-r}{r-1}~
p^r~(1-p)^{x-r}
\\[15pt]
&=~r(r+1)~p^r~\sum_{x=r}^{\infty}
\binom{x+1}{r+1}~(1-p)^{x-r}
\\[15pt]
&=~r(r+1)~p^r~\sum_{k=0}^{\infty}
\binom{k+r+1}{r+1}~(1-p)^k
\\[15pt]
&=~r(r+1)~p^r~\sum_{k=0}^{\infty}
\binom{(r+2)-1+k}{(r+2)-1}~(1-p)^k
\\[15pt]
&=~r(r+1)~p^r~\{1-(1-p)\}^{-(r+2)}
\\[15pt]
&=\dfrac{r(r+1)}{p^2}
\end{align*}
- Var(X)
\begin{align*}
Var(X) &= E(X^2)-E(X)^2
\\[15pt]
&= \bigg\{
\dfrac{r(r+1)}{p^2} -
\dfrac{r}{p}
\bigg\}
-\dfrac{r^2}{p^2}
\\[15pt]
&= \dfrac{r}{p^2}
~(r+1-p-r)
\\[15pt]
&= \dfrac{r(1-r)}{p^2}
\end{align*}
MGF
M_X(t) = \Bigg\{
\dfrac{pe^t}{1-e^t(1-p)}
\Bigg\}^r
\begin{align*}
M_X(t) &= E(e^{tX})
\\[15pt]
&=\sum_{x}~e^{tx}~
\binom{x-1}{r-1}~p^r~(1-p)^{x-r}
\\[20pt]
&=\sum_{x}~e^{t(x-r+r)}~
\binom{x-1}{r-1}~
p^r~(1-p)^{x-r}
\\[20pt]
&=e^{tr}~p^r~\sum_{x}
\binom{x-1}{r-1}
\Big\{e^t(1-p)\Big\}^{x-r}
\\[20pt]
&= (pe^t)^r~\sum_{x=0}^{\infty}
\dfrac{r+k-1}{r-1}
\big\{e^t(1-p)\big\}^n
\\[20pt]
&= \dfrac
{(pe^t)^r}
{\Big\{ 1-e^t(1-p) \Big\} ^r}
=
\bigg\{
\dfrac
{pe^t}
{1-e^t(1-p)}
\bigg\}^r
\end{align*}
log MGF
\begin{align*}
M_X(t) &= \Bigg\{
\dfrac{pe^t}{1-e^t(1-p)}
\Bigg\}^r
\\[20pt]
\log M_X(t)
&= r\log pe^t
- r\log (1-e^t(1-p))
\\[10pt]
&= r\log p + rt
- r\log (1-e^t(1-p))
\\[35pt]
\dfrac{d}{dt}~\log M_X(t)
&= r + \dfrac
{re^t(1-p)}{1-e^t(1-p)}
= r
\bigg(
1 + \dfrac
{e^t(1-p)}
{1-e^t(1-p)}
\bigg)
\\[20pt]
&=\dfrac{r}{1-e^t(1-p)}
\\[35pt]
\dfrac{d^2}{dt^2}~\log M_X(t) &=
\dfrac{d}{dt}~
\dfrac{r}{1-e^t(1-p)}
=
\dfrac{d}{dt}~
r\Big\{1-e^t(1-p)\Big\}^{-1}
\\[20pt]
&=r\cdot
\Big\{1-e^t(1-p)\Big\}^{-2}
\cdot(-1)
\cdot\Big\{-e^t(1-p)\Big\}
\\[20pt]
&=r~(1-p)~e^t
\Big\{1-e^t(1-p)\Big\}^{-2}
\end{align*}