Discrete Thoughts


An Upper Bound on Stirling Number of the Second Kind

We shall show an upper bound on the Stirling number of the second kind, a byproduct of a homework exercise of Probabilistic Combinatorics offered by Prof. Tom Bohman.

Definition. A Stirling number of the second kind (or Stirling partition number) is the number of ways to partition a set of $n$ objects into $k$ non-empty subsets and is denoted by $S(n,k)$.

Proposition. For all $n, k$, we have

$$S(n,k) \leq \frac{k^n}{k!}\left(1-(1-1/m)^k\right)^m.$$

Proof. Consider a random bipartite graph with partite sets $U:=[n], V:=[k]$. For each vertex $u\in U$, it (independently) connects to exactly one of the vertices in $V$ uniformly at random. Suppose $X$ is the set of non-isolated vertices in $V$. It is easy to see that

$$\operatorname{Pr}\left(X=V\right) = \frac{\text{number of surjections from }U\text{ to }V}{k^n} = \frac{k!S(n,k)}{k^n}.$$

On the other hand, we claim that for any $\emptyset \neq A \subset [k]$ and $i \in [k]\setminus A$,

$$\operatorname{Pr}\left(i\in X \mid A\subset X\right) \leq \operatorname{Pr}\left(i\in X\right).$$

Note that the claim is equivalent to

$$\operatorname{Pr}\left(A\subset X \mid i\notin X\right) \geq \operatorname{Pr}\left(A\subset X\right).$$

Consider the same random bipartite graph with $V$ replaced by $V':=[k]\setminus \{i\}$ and let $X'$ be the set of non-isolated vertices in $V'$. The claim is justified since

$$\operatorname{Pr}\left(A\subset X\mid i\notin X\right) = \operatorname{Pr}\left(A\subset X'\right) \geq \operatorname{Pr}\left(A\subset X\right).$$

Set $A:=[i-1]$ in above for $i = 2, \ldots, k$. Using the multiplication rule with telescoping the conditional probability, we obtain

$$\begin{aligned}\operatorname{Pr}\left(X=V\right) =& \operatorname{Pr}\left(1\in X\right)\operatorname{Pr}\left(2\in X \mid [1]\subset X\right) \\ & \ldots \operatorname{Pr}\left(k\in X\mid [k-1]\subset X\right)\\ \leq & \operatorname{Pr}\left(1\in X\right)\operatorname{Pr}\left(2\in X\right)\ldots\operatorname{Pr}\left(k\in X\right) \\ = & \left(1-(1-1/m)^k\right)^m.\end{aligned}$$
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