時間序列︰生成函數《十中》

一個知道范德蒙恆等式的人,

Vandermonde’s identity

In combinatorics, Vandermonde’s identity, or Vandermonde’s convolution, named after Alexandre-Théophile Vandermonde (1772), states that

{m+n \choose r}=\sum _{{k=0}}^{r}{m \choose k}{n \choose r-k},\qquad m,n,r\in {\mathbb {N}}_{0},

for binomial coefficients. This identity was given already in 1303 by the Chinese mathematician Zhu Shijie (Chu Shi-Chieh). See Askey 1975, pp. 59–60 for the history.

There is a q-analog to this theorem called the q-Vandermonde identity.

The general form of Vandermonde’s identity is

{\displaystyle {n_{1}+\dots +n_{p} \choose m}=\sum _{k_{1}+\cdots +k_{p}=m}{n_{1} \choose k_{1}}{n_{2} \choose k_{2}}\cdots {n_{p} \choose k_{p}}}.

 

是否就能用它來說明

\sum \limits_{l=0}^{n} {\left( \begin{array}{ccc} n \\ l  \end{array} \right)}^2 = {\left( \begin{array}{ccc} 2 n \\ n  \end{array} \right)}

 

假使他還通熟它的多種證明方法,

Proofs

Algebraic proof

In general, the product of two polynomials with degrees m and n, respectively, is given by

{\biggl (}\sum _{{i=0}}^{m}a_{i}x^{i}{\biggr )}{\biggl (}\sum _{{j=0}}^{n}b_{j}x^{j}{\biggr )}=\sum _{{r=0}}^{{m+n}}{\biggl (}\sum _{{k=0}}^{r}a_{k}b_{{r-k}}{\biggr )}x^{r},

where we use the convention that ai = 0 for all integers i > m and bj = 0 for all integers j > n. By the binomial theorem,

(1+x)^{{m+n}}=\sum _{{r=0}}^{{m+n}}{m+n \choose r}x^{r}.

Using the binomial theorem also for the exponents m and n, and then the above formula for the product of polynomials, we obtain

{\begin{aligned}\sum _{{r=0}}^{{m+n}}{m+n \choose r}x^{r}&=(1+x)^{{m+n}}\\&=(1+x)^{m}(1+x)^{n}\\&={\biggl (}\sum _{{i=0}}^{m}{m \choose i}x^{i}{\biggr )}{\biggl (}\sum _{{j=0}}^{n}{n \choose j}x^{j}{\biggr )}\\&=\sum _{{r=0}}^{{m+n}}{\biggl (}\sum _{{k=0}}^{r}{m \choose k}{n \choose r-k}{\biggr )}x^{r},\end{aligned}}

where the above convention for the coefficients of the polynomials agrees with the definition of the binomial coefficients, because both give zero for all i > m and j > n, respectively.

By comparing coefficients of xr, Vandermonde’s identity follows for all integers r with 0 ≤ r ≤ m + n. For larger integers r, both sides of Vandermonde’s identity are zero due to the definition of binomial coefficients.

Combinatorial proof

Vandermonde’s identity also admits a combinatorial double counting proof, as follows. Suppose a committee consists of m men and n women. In how many ways can a subcommittee of r members be formed? The answer is

  {m+n \choose r}.

The answer is also the sum over all possible values of k, of the number of subcommittees consisting of k men and r − k women:

  \sum _{{k=0}}^{r}{m \choose k}{n \choose r-k}.

Geometrical proof

Take a rectangular grid of r x (m+n-r) squares. There are

  {\binom {r+(m+n-r)}{r}}={\binom {m+n}{r}}

paths that start on the bottom left vertex and, moving only upwards or rightwards, end at the top right vertex (this is because r right moves and m+n-r up moves must be made (or vice versa) in any order, and the total path length is m+n). Call the bottom left vertex (0,0).

There are  {\binom {m}{k}} paths starting at (0,0) that end at (k,m-k), as k right moves and m-k upward moves must be made (and the path length is m). Similarly, there are  {\binom {n}{r-k}} paths starting at (k,m-k) that end at (r,m+n-r), as a total of r-k right moves and (m+n-r)-(m-k) upward moves must be made and the path length must be r-k + (m+n-r)-(m-k) = n. Thus there are

{\binom {m}{k}}{\binom {n}{r-k}}

paths that start at (0,0), end at (r,m+n-r), and go through (k,m-k). This is a subset of all paths that start at (0,0) and end at (r,m+n-r), so sum from k=0 to k=r (as the point (k,m-k) is confined to be within the square) to obtain the total number of paths that start at (0,0) and end at (r,m+n-r).

 

是否可以用它來推導『獨立隨機變數』之線性組合機率母函數︰

Probability generating functions are particularly useful for dealing with functions of independent random variables. For example:

  • If X1, X2, …, Xn is a sequence of independent (and not necessarily identically distributed) random variables, and
S_{n}=\sum _{i=1}^{n}a_{i}X_{i},
where the ai are constants, then the probability generating function is given by
G_{S_{n}}(z)=\operatorname {E} (z^{S_{n}})=\operatorname {E} (z^{\sum _{i=1}^{n}a_{i}X_{i},})=G_{X_{1}}(z^{a_{1}})G_{X_{2}}(z^{a_{2}})\cdots G_{X_{n}}(z^{a_{n}}).
For example, if
S_{n}=\sum _{i=1}^{n}X_{i},
then the probability generating function, GSn(z), is given by
G_{S_{n}}(z)=G_{X_{1}}(z)G_{X_{2}}(z)\cdots G_{X_{n}}(z).
It also follows that the probability generating function of the difference of two independent random variables S = X1X2 is
G_{S}(z)=G_{X_{1}}(z)G_{X_{2}}(1/z).

 

進而清楚解釋

如果 X, Y 是兩個獨立的二項分布隨機變數,那麼 Z = X + Y 也是一個二項分布隨機變數。

知其實無法闡釋下圖現象的嗎?