Hmh, I interpret standard nerual netwroks (which are the ones it focuses on) to be frequentist, since you are essentially maximising a likelihood without any priors and without an built-in uncertainty.
There’s the whole bayesian nn world where the focus is on being able to easily embed priors and treating every cell as a probability distribution and obtaining a probability distribution for every output cell (which is the important part).
In practice this doesn’t differ much, since you’re essentially just adding a few more terms to every weight and bias, but it seems to be a field that’s picking up speed… then again, I might just be stuck in my own reading bubble.
I guess upon further consideration I could scratch that whole thing, I’m honestly unsure if baesyan/frequentist is even a relevant distinction to be made anymore about modern ML/statistics/
Hmh, I interpret standard nerual netwroks (which are the ones it focuses on) to be frequentist, since you are essentially maximising a likelihood without any priors and without an built-in uncertainty.
There’s the whole bayesian nn world where the focus is on being able to easily embed priors and treating every cell as a probability distribution and obtaining a probability distribution for every output cell (which is the important part).
In practice this doesn’t differ much, since you’re essentially just adding a few more terms to every weight and bias, but it seems to be a field that’s picking up speed… then again, I might just be stuck in my own reading bubble.
I guess upon further consideration I could scratch that whole thing, I’m honestly unsure if baesyan/frequentist is even a relevant distinction to be made anymore about modern ML/statistics/