There are a few "flaws" to this solution actually, although the flaws are in assumptions + bayesian vs. frequentist philosophy differences. The solution mentio

There are a few "flaws" to this solution actually, although the flaws are in assumptions + bayesian vs. frequentist philosophy differences. The solution mentioned in the link solves a different problem: You get a sample of K ints uniform randomly from 1 to N, where N is unknown. Based on your sample, what is your best estimate of N, so that if we repeatedly continue to give you independent random samples, the average answer your algorithm gives will be the correct answer, and provides the minimum variance compared to other algorithms? However, if we can put an upper bound on the number of tanks that could possibly exist, say definitely no more than 100 million tanks, and if we believe it is slightly more likely that older tanks will be on the front lines than newer tanks, or any other prior knowledge, then the proposed solution will be more incorrect. One of my former co-workers proposed computing a posterior distribution for estimates of N based on equally-weighted priors for N = max-observation to N = maximum cap (100 million tanks). Then the estimate is the expected value of the posterior distribution.