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I have a scalar function, $g(x)$, where $x$ is an $n$-vector following a multinomial distribution with mass $f(x;p, N)$, for some probability-vector $p$, such that $\sum p_i=1$ and where $\sum x_i = N$.

Now, I'm interested in computing $E[g(x)]$, hence I write $$ E[g(x)] = \sum_{x_1=0}^N \sum_{x_2=0}^{N-x_1} \cdots \sum_{x_n=N-\sum_{i=1}^n x_i}^{N-\sum_{i=1}^n} g(x)f(x; p, N). $$

Now, this expression is rather complex, and it appears non-trivial to analyse the expected value, or higher moments, of this function.

So, my quesion is: Does anyone have experience with this kind of computation? Is it better to do Monte Carlo than to work analytically with the expression?

Perhaps you know of some papers or books that discuss this kind of problem?

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  • $\begingroup$ Depends on $g$. It might be helpful to write $g(x)=g(x_1+\cdots+x_N)$ where the $x_i$ take values in $\{0,1\}^n$, and to see what comes out. If you are lucky then linearity of expectation can be applied. $\endgroup$ – drhab May 25 '16 at 11:56
  • $\begingroup$ I'm not sure what you mean. Perhaps you can elaborate? The function $g$ is $\mathbb{R}^n \rightarrow \mathbb{R}$ and can be anything. In many cases, though, it is ``nice'' in the sense that it is linear, affine or polynomial and in almost all cases it is convex. Maybe that helps? Also, $N$ can be fairly large, at least a few thousands, but can be several orders larger, e.g. in the hundreds of thousands. $\endgroup$ – Tommy L May 26 '16 at 6:49
  • $\begingroup$ What I described has profit if $g$ is linear and, more generally if it is polynomial. $\endgroup$ – drhab May 27 '16 at 7:45
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For the special case where $n=2$ you have the binomial distribution, and the expectation of an arbitrary function of a binomial random variable is a mathematical function called the Bézier curve. This is usually computed using a recursive algorithm called DeCasteljau's algorithm (see this closely related answer). This recursive algorithm is used to prevent computational underflow problems that would occur if you attempted to use direct computation.

For the more general multinomial case, it is possible to extend this algorithm to arbitrary dimensions, though the algorithm becomes quite cumbersome. To obtain a recursive characterisation of the expectation, we take advantage of the well-known recursive equation for the multinomial distribution:

$$\text{Mu}(\mathbf{x}|N, \mathbf{p}) = \sum_{i=1}^n p_i \cdot \text{Mu}(\mathbf{x} - \mathbf{e}_i|N-1, \mathbf{p}).$$

Let $\mathscr{X}_N \equiv \{ \mathbf{x} \in \mathbb{N}_{0+}^n | \sum x_i = N \}$ denote the set of all possible count vectors with sum $N$, and let $\mathbf{e}_i$ denote the $i$th basis vector. Then, using the above recursive equation, for any function $g: \mathscr{X}_N \rightarrow \mathbb{R}$ you have:

$$\begin{equation} \begin{aligned} \mathbb{E}(g(\mathbf{X}) | \mathbf{X} \sim \text{Mu}(N, \mathbf{p}) ) &= \sum_{\mathbf{x} \in \mathscr{X}_N} g(\mathbf{x}) \cdot \text{Mu}(\mathbf{x}|N, \mathbf{p}) \\[6pt] &= \sum_{\mathbf{x} \in \mathscr{X}_N} g(\mathbf{x}) \cdot \sum_{i=1}^n p_i \cdot \text{Mu}(\mathbf{x} - \mathbf{e}_i|N-1, \mathbf{p}) \\[6pt] &= \sum_{i=1}^n p_i \cdot \sum_{\mathbf{x} \in \mathscr{X}_N} g(\mathbf{x}) \cdot \text{Mu}(\mathbf{x} - \mathbf{e}_i|N-1, \mathbf{p}) \\[6pt] &= \sum_{i=1}^n p_i \cdot \sum_{\mathbf{x} \in \mathscr{X}_{N-1}} g(\mathbf{x}+\mathbf{e}_i) \cdot \text{Mu}(\mathbf{x}|N-1, \mathbf{p}) \\[6pt] &= \sum_{i=1}^n p_i \cdot \sum_{\mathbf{x} \in \mathscr{X}_{N-1}} \triangleleft_i g(\mathbf{x}) \cdot \text{Mu}(\mathbf{x}|N-1, \mathbf{p}) \\[6pt] &= \sum_{i=1}^n p_i \cdot \mathbb{E}(\triangleleft_i g(\mathbf{X}) | \mathbf{X} \sim \text{Mu}(N-1, \mathbf{p}) ) \\[6pt] \end{aligned} \end{equation}$$

where $\triangleleft_i g : \mathscr{X}_{N-1} \rightarrow \mathbb{R}$ is a new function defined by $\triangleleft_i g(\mathbf{x}) = g(\mathbf{x}+\mathbf{e}_i)$. This gives you a recursive equation for the expectation, where each iteration of the expectation reduces the size $N$ by one, but requires you to split into $n$ terms. In Decasteljau's algorithm (for the binomial case) you arrange your values in a matrix and compute them recursively from the initial values of the function $g$ for $N=1$ (see the linked answer above for specifics). In the more general multinomial case you can put your values in an $n$ dimensional array and compute the values recursively from the initial values of the function $g$ for $N=1$. This would be done using the above recursive algorithm.

This should give you an algorithm that will compute your expectation exactly, without leading to underflow problems. In the event that the expectations are very small, you can do your computing on a log-scale to prevent underflow. The algorithm is order $\mathcal{O}(N^n)$, so if either of these values are large, then the algorithm might become computationally infeasible. In this latter case you would fall back onto Monte Carlo simulation.

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This is not a real answer, but an effort to make clear what I said in my comment.

What I say now about binomial will work also for multinomial.

If e.g. the vector $\left(X,Y\right)$ has binomial distribution (see note below) with parameters $\left(p,q\right)$ and $N$, then you can write $\left(X,Y\right)=\sum_{i=1}^{N}\left(X_{i},Y_{i}\right)$ where the $\left(X_{i},Y_{i}\right)$ are iid and have binomial distribution with parameters $\left(p,q\right)$ and $1$.

That might help to determine $\mathbb{E}g\left(X,Y\right)$. If for instance $g$ is linear then we have: $$g\left(X,Y\right)=g\left(\sum_{i=1}^{N}\left(X_{i},Y_{i}\right)\right)=\sum_{i=1}^{N}g\left(X_{i},Y_{i}\right)$$ and with linearity of expectation combined with symmetry we find: $$\mathbb{E}g\left(X,Y\right)=\mathbb{E}\sum_{i=1}^{N}g\left(X_{i},Y_{i}\right)=\sum_{i=1}^{N}\mathbb{E}g\left(X_{i},Y_{i}\right)=N\mathbb{E}g\left(X_{1},Y_{1}\right)$$ It can be quite easy to find $\mathbb Eg(X_1,Y_1)$.

Essential is that expectations are found without making use of the usual (but often unconvenient) summation $\mu=\sum xp(x)$.


note: In cases like this we only work with $X$ because $Y=N-X$ hence is completely determined by $X$. The split up is then $X=\sum_{i=1}^{N}X_i$ where the $X_i$ have Bernoulli-distribution.

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