# Coupon Collector Problem with Batched Selections

I am trying to solve a variation on the coupon collector's problem.

In this scenario, someone is selecting coupons at random with replacement from n different possible coupons. However, the person is not selecting coupons one at a time, but instead, in batches.

Here's an example problem formulation: There are 100 distinct coupons. A person makes selections in 10-coupon batches at random (each coupon with replacement). What is the expected number of batches necessary to have selected 80 unique coupons?

I have been able to determine the expected number of selections necessary to have selected k unique coupons when selecting one at a time (much like Henry's answer to a similar question), but I'm a bit stumped as to how to go about solving it with this particular wrinkle.

Any tips/guidance would be greatly appreciated.

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Let $N_t$ denote the number of different coupons after the selection of $t$ batches of size $b$ drawn from $n$ different coupons and $T_k=\inf\{t\geqslant0\mid N_t\geqslant k\}$ (here, $b=10$, $n=100$ and $k=80$). One is interested in $$\mathrm E_{b,n}(T_k)=\sum\limits_{t\geqslant0}\mathrm P_{b,n}(T_k\gt t)=\sum\limits_{t\geqslant0}\mathrm P_{b,n}(N_t\lt k).$$ The way to compute the value of $\mathrm E_{b,n}(T_k)$ is unclear, although the dynamics of $(N_t)_t$ is easy to describe.
To wit, $N_0=0$, $N_1=b$, and if $N_t=i$, $N_{t+1}=i+M_t$ where $0\leqslant M_t\leqslant b$ almost surely and the distribution of $M_t$ depends on $i$ and may be written down. For example, if $b=2$, $M_t=0$ with probability $\frac{i(i-1)}{n(n-1)}$, $M_t=1$ with probability $\frac{2i(n-i)}{n(n-1)}$ and, finally, $M_t=2$ with probability $\frac{(n-i)(n-i-1)}{n(n-1)}$. Thus, $\mathrm E_{2,n}(M_t\mid N_t)=2(1-N_t/n)$ and $\mathrm E_{2,n}(N_t)=n(1-(1-2/n)^t)$.
More generally, $\mathrm E_{b,n}(M_t\mid N_t)=b(1-N_t/n)$ and $\mathrm E_{b,n}(N_t)=n(1-(1-b/n)^t)$.
One might want to estimate $\mathrm E_{b,n}(T_k)$ by the root $t_{b,n}(k)$ of the equation $k=\mathrm E_{b,n}(N_t)$, that is, $$t_{b,n}(k)=\frac{\log(1-k/n)}{\log(1-b/n)},$$ for example, $t_{10,100}(80)\approx15.28$. However, the quality of this approximation (or rather, the range of parameters $(n,b,k)$ where it is passable) should be checked, numerically or otherwise.
Thank you for the thorough answer. Let me clarify one specification of the problem: There could be duplicates in each batch. That is, $N_1\leqslant b$ as collisions are possible after the first selection. The batch here acts only as a limit on how often one can check to see if the number of unique coupons selected has met or exceeded $k$. Any thoughts on that? –  rukh Apr 14 '12 at 20:42
After thinking about this with my comment above in mind, it seems like a good approximation would be taking the ceiling of the expected number of trials divided by batch size: $$n \log_e \left(\frac{n}{n-p}\right)$$ divided by $b$. Thoughts? –  rukh Apr 14 '12 at 21:08