Generating random numbers following a given discrete probability distribution
I have never really thought very much about generating random numbers according to a precise discrete distribution, for example to simulate an unfair dice.
In finance, we are generally interested in continuous distributions, where there is typically 2 ways:
- the inverse transform (usually computed in a numerical way),
- and the acceptance-rejection method, typically the ziggurat.
The inverse transform is often preferred, because it’s usable method for Quasi Monte-Carlo simulations while the acceptance rejection is not.I would have thought about the simple way to generate random numbers according to a discrete distribution as first described here. But establishing a link with Huffman encoding is brilliant. Some better performing alternative (unrelated to Huffman) is offered there.