The multinomial distribution is a generalization of the binomial distribution to k categories instead of just binary (success/fail). For n independent trials each of which leads to a success for exactly one of k categories, the multinomial distribution gives the probability of any particular combination of numbers of successes for the various categories

Example: Rolling a die N times

In Data mining, When we discuss everything in terms of text classification, i.e. Topic Modeling:

Each document has its own distribution over topics. Each topic has its own distribution over the words.

The (multinomial) distribution over words for a particular topic The (multinomial) distribution over topics for a particular document

Two chess players have the probability Player A would win is 0.40, Player B would win is 0.35, game would end in a draw is 0.25.

The multinomial distribution can be used to answer questions such as: “If these two chess players played 12 games, what is the probability that Player A would win 7 games, Player B would win 2 games, the remaining 3 games would be drawn?”

`dmultinom(x=c(7,2,3), prob = c(0.4,0.35,0.25))`

`## [1] 0.02483712`

In a little town, 40% of the eligible voters prefer candidate A, 10% prefer candidate B, 50% have no preference.

You randomly sample 10 eligible voters. What is the probability that 4 will prefer candidate A, 1 will prefer candidate B, 5 will have no preference ?

`dmultinom(x=c(4,1,5), prob = c(0.4,0.1,0.5))`

`## [1] 0.1008`

A distribution that shows the likelihood of the possible results of a experiment with repeated trials in which each trial can result in a specified number of outcomes that is greater than two. A multinomial distribution could show the results of tossing a dice, because a dice can land on one of six possible values. By contrast, the results of a coin toss would be shown using a binomial distribution because there are only two possible results of each toss, heads or tails.

Two additional key characteristics of a multinomial distribution are that the trials it illustrates must be independent (e.g., in the dice experiment, rolling a five does not have any impact on the number that will be rolled next) and the probability of each possible result must be constant (e.g., on each roll, there is a one in six chance of any number on the die coming up).

```
one.dice <- function(){
dice <- sample(1:6, size = 1, replace = TRUE)
return(dice)
}
one.dice()
```

`## [1] 4`

`one.dice()`

`## [1] 2`

`one.dice()`

`## [1] 3`

```
par(mfrow=c(2,2))
for (i in 1:4){
sims <- replicate(100, one.dice())
table(sims)
table(sims)/length(sims)
plot(table(sims), xlab = 'Event', ylab = 'Frequency')
}
```

```
par(mfrow=c(2,2))
for (i in 1:4){
sims <- replicate(10000, one.dice())
table(sims)
table(sims)/length(sims)
plot(table(sims), xlab = 'Event', ylab = 'Frequency')
}
```

Generate Multinomial Random Variables With Varying Probabilities Given a matrix of multinomial probabilities where rows correspond to observations and columns to categories (and each row sums to 1), generates a matrix with the same number of rows as has probs and with m columns. The columns represent multinomial cell numbers, and within a row the columns are all samples from the same multinomial distribution.

```
# rmultinom(n, size, prob)
my_prob <- c(0.2,0.3,0.1,0.4)
number_of_experiments <- 10
```

```
number_of_samples <- 10
experiments <- rmultinom(n=number_of_experiments, size=number_of_samples, prob=my_prob)
experiments
```

```
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 4 1 4 0 0 3 3 2 2
## [2,] 4 3 7 2 3 3 1 0 1 5
## [3,] 1 0 0 0 0 0 2 0 1 2
## [4,] 4 3 2 4 7 7 4 7 6 1
```

```
df=data.frame(experiments)/number_of_samples
par(mfrow=c(2,5))
for(i in 1:10) {
barplot(df[,i],ylim=c(0,1))
}
```

```
number_of_samples <- 1000
experiments <- rmultinom(n=number_of_experiments, size=number_of_samples, prob=my_prob)
experiments
```

```
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 184 193 182 188 194 196 193 182 211 194
## [2,] 301 300 309 278 296 302 274 288 327 307
## [3,] 98 86 103 103 101 105 113 105 97 98
## [4,] 417 421 406 431 409 397 420 425 365 401
```

```
df=data.frame(experiments)/number_of_samples
par(mfrow=c(2,5))
for(i in 1:10) {
barplot(df[,i],ylim=c(0,1))
}
```