Solution

ID: past-exam-of-the-computer-science-course-of-the-university-of-oxford/2015/probability-and-computing/2/d/solution

If the bound in part (c) were available, the intended answer would be
Indeed , so that bound would give . Its growth is in Big O notation. Because (c) is false, this substitution alone does not prove the result for the actual repeated singleton-removal allocation.
The requested order can nevertheless be established for the actual process. Here is a concrete, deliberately generous replacement, with for the empty system:
The large cutoff makes the numerical estimates below simple. A finite initial range does not affect the asymptotic analysis.
First, whenever , a ball is served with probability at least
Thus part (a) gives , also for . The law of total expectation yields . This proves for the small- branch, including , whose single ball is served immediately.
For the large- branch, we need control of the random count, rather than an unjustified recurrence for its expectation. If balls are allocated in a round, changing one chosen bin changes the number of singleton bins by at most two. The surviving count therefore has the bounded differences property with constants . McDiarmid's inequality gives
This can be applied conditionally after any history, since the next allocations are fresh independent random variables.
Use this concentration inequality in three stages. Write .
  • For the first eight rounds, whenever , the conditional expectation is at most . Taking shows that the next count is at most , except with probability at most . If the count has already fallen below , it stays there. Since and , this gives outside the stated exceptional events.
  • Starting with , set . If and , part (a) and the union bound give . Taking gives a next count at most , except with probability at most . The deterministic solution is . It falls below within steps. After reaching that threshold the count cannot increase, so outside the exceptional events.
  • From , the next conditional expectation is at most . A deviation leaves at most balls except with probability . From at most balls, the next conditional expectation is at most . Taking leaves at most balls except with probability . Conditional on this final count bound, one more round has expectation at most .
Every exceptional probability above is at most , and there are at most opportunities for an exception. For , the elementary bounds and give, by the union bound, total exceptional probability at most . The first numerical inequality holds at the cutoff and remains true because is increasing there. On an exceptional history there are at most balls. Taking the last round's conditional expectation and then averaging gives
This proves the displayed replacement for the literal process without relying on either false assertion.

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