From Jason Turner

[rand.dist.samp.discrete]

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  ##### Class template `discrete_distribution` <a id="rand.dist.samp.discrete">[[rand.dist.samp.discrete]]</a>
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  A `discrete_distribution` random number distribution produces random
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  integers i, 0 ≤ i < n, distributed according to the discrete probability
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- function $$P(i \,|\, p_0, \dotsc, p_{n-1}) = p_i \text{ .}$$
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  Unless specified otherwise, the distribution parameters are calculated
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  as: pₖ = {wₖ / S} for k = 0, …, n - 1, in which the values wₖ, commonly
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  known as the *weights* , shall be non-negative, non-NaN, and
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  non-infinity. Moreover, the following relation shall hold:
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  requirements [[input.iterators]]. If `firstW == lastW`, let n = 1 and
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  w₀ = 1. Otherwise, [`firstW`, `lastW`) forms a sequence w of length
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  n > 0.
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  *Effects:* Constructs a `discrete_distribution` object with
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- probabilities given by the formula above.
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  ``` cpp
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  discrete_distribution(initializer_list<double> wl);
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  ```
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  ##### Class template `discrete_distribution` <a id="rand.dist.samp.discrete">[[rand.dist.samp.discrete]]</a>
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  A `discrete_distribution` random number distribution produces random
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  integers i, 0 ≤ i < n, distributed according to the discrete probability
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+ function in .
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  Unless specified otherwise, the distribution parameters are calculated
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  as: pₖ = {wₖ / S} for k = 0, …, n - 1, in which the values wₖ, commonly
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  known as the *weights* , shall be non-negative, non-NaN, and
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  non-infinity. Moreover, the following relation shall hold:
 
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  requirements [[input.iterators]]. If `firstW == lastW`, let n = 1 and
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  w₀ = 1. Otherwise, [`firstW`, `lastW`) forms a sequence w of length
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  n > 0.
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  *Effects:* Constructs a `discrete_distribution` object with
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+ probabilities given by the .
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  ``` cpp
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  discrete_distribution(initializer_list<double> wl);
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  ```
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