# Multinomial distribution

I am not sure How Stan caculates multinomial distribution.
I did not know the multinomial distribution, then I model it by reducing it to binomials.
My question is the following two model is equivalent or not in Stan.

In my model, there is a random variable X=(X_1,X_2,X_3.X_4), X_i\geq0, \sum X_i =n.
Let p=(p_1,p_2,p_3), p_i >0, \sum p_i =1.

Then, the multinomial distribution is

### 1st model

X \sim \text{Multinomial}(p)

On the other hand, we can reduce the above to binomial distributions as follows

### 2nd model

X_3 \sim \text{Binomial}(p_3,n)

X_2 \sim \text{Binomial}(\frac{p_2}{1-p_3},n-X_3)

X_1 \sim \text{Binomial}(\frac{p_1}{1-p_3-p_2},n-X_3-X_2)

X_4:= n-X_1-X_2-X_3.

This reduction is also used, e.g., ?rmultinom in stats package.

I use the above both model in my model, then the behavour of HMC is not same. E.g., for some data, one of them converges but the other dose not.

In Stan, the multinomial distribution is defiend using the above reduction to binomials? Namely, the above two are equivalent?

Are you sure the parameterization are the same order?

This is what I got and the log densities in R evaluate to the same thing:

prob = c(0.1, 0.2, 0.3, 0.4)

N = 20

x = rmultinom(1, N, prob)

dmultinom(x, prob = prob, log = TRUE)

dbinom(x, N, prob, log = TRUE) +
dbinom(x, N - x, prob / (1 - prob), log = TRUE) +
dbinom(x, N - x - x, prob / (1 - prob - prob), log = TRUE)


In my terms the binomials are in a different order.

I know that the the reduction of multinomials to binomials depends on the order of X_i.

I wander is there some order in which Stan reduce a multinomial to binomials. The convergence of Stan (e.g., in \hat{R} and divergence transition) is different between “~ multinomial()” and many “ ~ biomial()-statements” in my order.

Can I say that the multinomial differes to the binomials? Or, Is it essentially same?

I initially thought these were different, but coincidentally found the same reduction in ?rmultinom() in the stats package. Is this widely used in statistics or also used in Stan (e.g., multinomial_rng() and sampling statement)?

I tried this out and it’s working okay for me.

Here is the model I used:

data {
int y;
}

transformed data {
int N = sum(y);
}

parameters {
simplex p1;
simplex p2;
}

model {
y ~ multinomial(p1);

y ~ binomial(N, p2);
y ~ binomial(N - y, p2 / (1 - p2));
y ~ binomial(N - y - y, p2 / (1 - p2 - p2));
}

generated quantities {
real lp1 = multinomial_lpmf(y | p1);
real lp2 = binomial_lpmf(y | N, p1) +
binomial_lpmf(y | N - y, p1 / (1 - p1)) +
binomial_lpmf(y | N - y - y, p1 / (1 - p1 - p1));
real diff = lp1 - lp2;
}


The output is:

> fit$print(max_rows = 12) variable mean median sd mad q5 q95 rhat ess_bulk ess_tail lp__ -66.22 -65.87 1.79 1.63 -69.64 -63.91 1.00 1901 2329 p1 0.13 0.12 0.07 0.06 0.04 0.25 1.00 5113 2879 p1 0.21 0.20 0.08 0.08 0.09 0.35 1.00 5920 2935 p1 0.29 0.29 0.09 0.09 0.15 0.44 1.00 5858 3321 p1 0.37 0.37 0.10 0.10 0.22 0.54 1.00 5418 3137 p2 0.12 0.11 0.07 0.07 0.03 0.25 1.00 5221 2453 p2 0.21 0.20 0.08 0.08 0.09 0.36 1.00 5160 2678 p2 0.29 0.29 0.09 0.10 0.15 0.46 1.00 5302 3223 p2 0.37 0.37 0.10 0.10 0.22 0.54 1.00 5934 3311 lp1 -5.60 -5.30 1.05 0.85 -7.72 -4.46 1.00 2126 2897 lp2 -5.60 -5.30 1.05 0.85 -7.72 -4.46 1.00 2126 2897 diff 0.00 0.00 0.00 0.00 0.00 0.00 1.00 3902 3913  So diff is zero if I calculate the lpmf using one method or the other. Also the inferences on p1 and p2 look vaguely the same. Here’s the R code: library(cmdstanr) prob = c(0.1, 0.2, 0.3, 0.4) N = 20 y = as.vector(rmultinom(1, N, prob)) model = cmdstan_model("multinom.stan") fit = model$sample(data = list(y = y))
fit\$print(max_rows = 12)

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