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2.6(難) 非負の離散確率変数の期待値 (方針で二重和の計算を解説)

Last updated at Posted at 2021-07-15

方針

初見で解くのは不可能.

k=1+...+1=\sum_{k=0}^{k-1}1

であることを用いる.二重和についての公式が必要になるので,シグマの公式や二重和の意味について確認しておく.

シグマ公式(平行移動)


\sum_{i=1}^na_i=\sum_{i=k+1}^{n+k}a_{i-k}

上端,下端を同方向にずらして添字を逆方向にずらす.


二重和の意味


  1. 全ての要素a_ijの和.
\sum_{i=1}^m\sum_{j=1}^na_{ij}=\sum_{i=1}^m(a_{i1}+...+a_{in})
  1. i=>jなる全ての要素a_ijの和.
\begin{align}
\sum_{i=1}^n\sum_{j=1}^ia_{ij}&= \sum_{i=1}^n(a_{i1}+...+a_{ii})\\
&= (a_{11})+(a_{21}+a_{22})+(a_{31}+a_{32}+a_{33})+...+(a_{n1}+...+a_{nn})
\end{align}

二重和の計算


  1. a_ijの総和は可換,
\sum_{i=1}^m\sum_{j=1}^na_{ij}=\sum_{j=1}^n\sum_{i=1}^ma_{ij}
  1. i=>jなる全てのa_ij (ただし要素は無限) の総和は以下のように可換.
\sum_{i=1}^\infty\sum_{j=1}^ia_{ij}=\sum_{j=1}^\infty\sum_{i=j}^\infty a_{ij}

答案

\begin{align}
\mathbb{E}_{X\sim f_X}[X]&= \sum_{k=0}^\infty kP[X=k]\\
&= \sum_{k=0}^\infty(\sum_{i=0}^{k-1}1)P[X=k]\\
&= \sum_{k=0}^\infty\sum_{i=0}^{k-1}P[X=k]\\
&= \sum_{i=0}^\infty\sum_{k=i+1}^\infty P[X=k]\\
&= \sum_{i=0}^\infty(P[X=i+1]+P[X=i+2]+...)\\
&= \sum_{i=0}^\infty(1-P[X\leq i])\\
&= \sum_{k=0}^\infty(1-F(k))
\end{align}

参考文献

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