(related to Proposition: Multinomial Distribution)

Imagine, during the \(n\) repetitions we first observe \(k_1\) realizations of the event \(A_1\), next we observe \(k_2\) realizations of the event \(A_2\), etc. and at the end we observe \(k_r\) realizations of the event \(A_r\). Then we will observe the joint event of the ordered occurrence of the realizations

\[C=(\underbrace{A_1,\ldots,A_1}_{k_1\text{ times}},\underbrace{A_2,\ldots,A_2}_{k_2\text{ times}},\ldots,\underbrace{A_r,\ldots,A_r}_{k_r\text{ times}}).\]

Clearly, we have \(k_i\ge 0\) for and \(k_1+k_2+\ldots+k_r=n\). Because all repetitions of the random experiment are mutually independent by hypothesis, we have for the probability of \(C\):

\[p(C )=p(A_1)^{k_1}p(A_2)^{k_2}\ldots p(A_r)^{k_r}.\]

Now, without taking into account the order of observed realizations of events \(A_i\), we define the event \(S_{k_1k_2\ldots k_r}\) by \[S_{k_1k_2\ldots k_r}:=\cases{A_1&\text{occurred }k_1\text{ times}\\\vdots\\A_r&\text{occurred }k_r\text{ times}\\}\]

\(S_{k_1k_2\ldots k_r}\) can be retrieved from \(C\) by rearranging the order of its realizations, while the probability of each rearrangement does not change. The number of such rearrangements is given by the multinomial coefficient. Thus, we have

\[p(S_{k_1k_2\ldots k_r})=\binom {n}{k_1,k_2,\ldots,k_r}p_1^{k_1}p_2^{k_2}\ldots p_r^{k_r}.\]

Therefore, the probability mass function of all random variables is given by

\[p(X_1=k_1,\ldots,X_r=k_r)=\cases{\binom {n}{k_1,k_2,\ldots,k_r}p_1^{k_1}p_2^{k_2}\ldots p_r^{k_r}&\text{if } k_1+k_2+\ldots+k_r=n\\ 0&\text{else},}\]

where \(X_1,\ldots,X_r\) are random variables counting the numbers \(k_i\) of the realizations of each event \(A_i\), \(i=1,\ldots,r\).

Because all events \(A_1,A_2,\ldots,A_r\) are collectively exhaustive and mutually exclusive events, we have

\[A_1\cup A_2\cup\ldots\cup A_r=\Omega.\]

Therefore, it follows from the multinomial theorem. \[p(\Omega)=1=(p_1 + p_2 + \ldots + p_r)^n=\sum_{\substack{k_1+\ldots+k_r=n \\ k_1,\ldots,k_r}}\binom{n}{k_1,k_2\ldots,k_r}p_1^{k_1} p_2^{k_2}\ldots p_r^{k_r}.\]

Thank you to the contributors under CC BY-SA 4.0!




  1. Bosch, Karl: "Elementare Einf├╝hrung in die Wahrscheinlichkeitsrechnung", vieweg Studium, 1995, 6th Edition