probability - Coupon Collector's Problem with X amount of coupons already collected.

I am having an issue with understanding how to calculate a specific case of the Coupon Collector's Problem. Say I have a set of 198 coupons. I learned how to find the estimated amount of draws to see all 198 coupons, using the following equation:

$$n \sum_{k=1}^n\frac1k$$

It turns out that for $n = 198$, the expected number of draws is approximately 1162. Let's assume, however, that I already have some of the coupons, say 50. How should I go about solving the same problem, given that I've already collected $X$ of them?

Answer

Based on the corresponding thread on Wikipedia. The expected time to draw all $n$ coupons equals:

$$E[T] = E[t_1] + E[t_2] + \ldots + E[t_n]$$

with $t_i$ the time needed to collect the $i^{th}$ coupon once $i-1$ coupons have been drawn. Once $i-1$ tickets have been drawn, there are $n-i+1$ unseen coupons left. The probability $p_i$ of selecting a new coupon thus equals $\frac{n-i+1}{n}$, and the expected number of draws needed to draw a new coupon equals $\frac{1}{p_i} = \frac{n}{n-i+1}$. As such, the expected value for the time needed to draw all $n$ coupons can be calculated as:

$$E[T] = \frac{n}{n} + \frac{n}{n-1} + \ldots + \frac{n}{1} = n \sum_{k=1}^{n}{\frac{1}{k}}$$

In this case, however, we have already drawn $X$ unique coupons. As such, the estimated number of draws needed to find all $n$ coupons equals:

$$E[T] = E[t_{X+1}] + E[t_{X+2}] + \ldots + E[t_n] = n \sum_{k=1}^{n-X} \frac{1}{k}$$