# 各以1/2的概率输出0和1

在看CLRS中的第五章，发现课后的这道习题非常有意思。

题目：Suppose that you want to output 0 with probability 1/2 and 1 with probability 1/2. At your disposal is a procedure BIASED-RANDOM, that outputs either 0 or 1. It outputs 1 with some probability p and 0 with probability 1 – p, where 0 < p < 1, but you do not know what p is. Give an algorithm that uses BIASED-RANDOM as a subroutine, and returns an unbiased answer, returning 0 with probability 1/2 and 1 with probability 1/2. What is the expected running time of your algorithm as a function of p? 解答：（标准答案） To get an unbiased random bit, given only calls to BIASED-RANDOM, call BIASED-RANDOM twice. Repeatedly do so until the two calls return different values, and when this occurs, return the Þrst of the two bits: UNBIASED-RANDOM while TRUE do x ← BIASED-RANDOM y ← BIASED-RANDOM if x ≠ y then return x To see that UNBIASED-RANDOM returns 0 and 1 each with probability 1/2, observe that the probability that a given iteration returns 0 is Pr {x = 0 and y = 1} = (1 − p)p , and the probability that a given iteration returns 1 is Pr {x = 1 and y = 0} = p(1 − p) . (We rely on the bits returned by BIASED-RANDOM being independent.) Thus, the probability that a given iteration returns 0 equals the probability that it returns 1. Since there is no other way for UNBIASED-RANDOM to return a value, it returns 0 and 1 each with probability 1/2. Assuming that each iteration takes O(1) time, the expected running time of UNBIASED-RANDOM is linear in the expected number of iterations. We can view each iteration as a Bernoulli trial, where success means that the iteration returns a value. The probability of success equals the probability that 0 is returned plus the probability that 1 is returned, or 2p(1 − p). The number of trials until a success occurs is given by the geometric distribution, and by equation (C.31), the expected number of trials for this scenario is 1/(2p(1 − p)). Thus, the expected running time of UNBIASED-RANDOM is θ(1/(2p(1 − p)).

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