It might be worth looking into probabilistic programming languages. I'm out of date, but I remember webppl, stan, anglican, pymc (a python library).
Seems worth an investigation and maybe mention on the article.
It might be worth looking into probabilistic programming languages. I'm out of date, but I remember webppl, stan, anglican, pymc (a python library).
Seems worth an investigation and maybe mention on the article.
if you read the website, the author explicitly mentions stan in the comparison at the end ;^)
It says
Stan and PyMC beat Noise at the thing they’re built for, fitting a posterior to lots of continuous data with their HMC/NUTS samplers, and NumPy beats it at raw array crunching. Conditioning in Noise is rejection-based, so it works great for a handful of discrete observations but becomes useless for ten thousand continuous measurements, and there is no stateful simulation yet (no Markov chains yet). Where Noise wins when you have a probability question and you wanna know the answer without much hassle.
So use Noise for the whiteboard stage of a problem, when you want to run the math you just wrote, and move to Stan or PyMC when you need a real posterior, or to NumPy and JAX when you need to go to production.