# A multinomial problem (balls, bins, etc.)

Consider the well known multinomial setting: there are L balls, thrown at random at n bins so that the probability that a ball falls in bin i is $p_i$, independent of the other balls (the $p_i$’s are all positive and sum to 1).

Let $X_i$ be the number of balls in the ith bin. It is straightforward to show that for any k distinct indices $i_1, i_2,\ldots, i_k$, the events $\lbrace X_{i_1} > 0\rbrace, \lbrace X_{i_2} > 0\rbrace,\ldots, \lbrace X_{i_k} > 0\rbrace$ are negatively dependent in the sense that

$P(X_{i_1} > 0, X_{i_2} > 0,\ldots, X_{i_k} > 0) < P(X_{i_1} > 0)P(X_{i_2} > 0)\ldots P(X_{i_k} > 0)$

The proof is by induction over k: for k = 2, the probability on the LHS is $1 – [(1 - p_{i_1})^L + (1 - p_{i_2})^L - (1 - p_{i_1} - p_{i_2})^L]$, and on the RHS it’s $[1 - (1 – p_{i_1})^L] [1 - (1 – p_{i_2})^L]$. Elementary manipulations show that the inequality holds, and the induction step is easily proven using the multiplication rule $P(A \cap B) = P(A)P(B | A)$.

This negative dependence is also intuitively clear: if we know that there is at least one ball in bin i, there is at least one ball that will surely not fall into bin j, so the probability of bin j being non-empty decreases.

So that was easy. However, for proving a certain bound in my research, I need to do something similar, but for random k indices: suppose that after the balls were thrown into the bins, somebody comes and chooses randomly k distinct bins (so that each set of k bins has the same probability to be chosen, namely, 1/(n choose k)). Let $i_1, i_2, \ldots, i_k$ be the chosen indices, and again, I need to show that the inequality (below the second paragraph above) holds.

I thought to repeat the outline of the previous proof, but had a problem with proving the k = 2 case (the induction step is no problem). To compute each of the sides of the inequality I conditioned on the choice of the two indices (law of total probability), but even though many terms got cancelled, I couldn’t prove the inequality.

I am quite confident that the inequality holds, both by intuition (basically the same argument as in the non-random indices case) and because of numerical calculations.

Any ideas what to do here? I’d be grateful for any help.

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may i ask you what happen to a bin if it is not chosen after the balls are thrown? Because in conditioning the probability changes.. For example if we say: "a bin which is not chosen is cleared out", may be completely different from "nothing happens to a bin if it is not chosen." This appears when you want to write down the total probability law. So can you explain more? Thanks.. – uforoboa Sep 22 '11 at 14:31
Since you already proved it holds for any $k$ distinct indices, why would you have to do anything else to prepare for someone coming along and randomly telling you which $k$ indices they need it to be true for? – Matt Apr 18 '12 at 21:30

## 1 Answer

Strangely enough, this is the unanswered question with most upvotes under the probability tag, even though the straightforward answer has already been provided in a comment quite a while ago.

Since the bins are all equivalent, the probabilities on both sides of the inequality are invariant under permutations of the bins. Thus, randomly choosing the indices leads to the same probabilities as fixing them, so the inequality holds as in the case of fixed indices.

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