The menu of probability distributions in numpy #numpy #computer #python #maths

Sampling using the numpy (python) ecosystem of continuous probability distributions
#numpy #computer #python #maths

This is a deleted scene from episode 2 but I thought I would share it here anyway as it might still be insightful for some. Episode 2 will be posted this summer (June 2025); it will be my submission for #some4.

One disclaimer: the Ziggurat algorithm used in numpy employs the uniform random variables coming from the PRNG more economically than I described here and it uses 256 rectangles instead of 6. In addition, the tail is not enclosed in any rectangle, contrary to the way in which I displayed it here. The animation of the Ziggurat as I have it here might still be useful in terms of how a simplified toy Ziggurat algorithm works but do not take it as an accurate description of how it is implemented in numpy (or any other language). Numpy uses the PRNG numbers more economically than described here. An accurate depiction of the Ziggurat algorithm employed by numpy will be included in episode 2. I already have another video up with the exact Ziggurat used by numpy to sample from standard normal. Please refer to it for the exact Ziggurat shape used by numpy.

Please note that I will delete this video once episode 2 is up as it will cover everything covered here, more correctly/precisely and more.

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