Add cum_weights example (simulation of a cumulative binomial distribution).
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@ -351,6 +351,13 @@ Basic usage::
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>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
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['red', 'green', 'black', 'black', 'red', 'black']
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# Probability of getting 5 or more heads from 7 spins of a biased coin
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# that settles on heads 60% of the time.
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>>> n = 10000
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>>> cw = [0.60, 1.00]
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>>> sum(choices('HT', cum_weights=cw, k=7).count('H') >= 5 for i in range(n)) / n
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0.4169
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Example of `statistical bootstrapping
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<https://en.wikipedia.org/wiki/Bootstrapping_(statistics)>`_ using resampling
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with replacement to estimate a confidence interval for the mean of a small
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