Random numbers are used in Monte Carlo methods, stochastic calculus, and more. Real random numbers are difficult to produce, so in practice, we use pseudo-random numbers. Pseudo-random numbers are sufficiently random for most intents and purposes, except for some very exceptional instances, such as very accurate simulations. The random-numbers-associated routines can be located in the NumPy `random`

subpackage.

The core random-number generator is based on the Mersenne Twister algorithm (refer to https://en.wikipedia.org/wiki/Mersenne_twister).

Random numbers can be produced from discrete or continuous distributions. The distribution functions have an optional `size`

argument, which informs NumPy how many numbers are to be created. ...

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