Hi, I'm the author of these articles.

I'm a physicist, and it always bugged me that we use Hamiltonians/energy/interactions to define spin glasses. I wondered if it'd be possible to just use raw probabilities on a grid and get the same/similar behaviour (eg. phase transitions, long-range correlations, etc).

So I played around with this idea for quite a while by running MC simulations, and ended up writing these articles (on my blog) about it.

But beware (i) none of this is peer-reviewed and (ii) I'm not a practicing academic physicist [so I may have missed things that would be obvious to a practicing academic physicist].

If anybody is interested in this, hit me up, happy to speak and/or play with the topic again.

What do you mean peer reviewed? This is the standard textbook way of numerically studying Ising models.

Re peer review: nobody has proof-read what I wrote or checked the code for bugs.

Re standard textbook: I've never seen a physics textbook define spin glasses using just probabilities, without the energy picture.

Spin glasses are a proposed model of learning in Neural Nets[0] along with state changes and sand pile collapses.

So this paper is worth reading if you study learning in neural nets

[0] Spin-glass models of neural networks Amit et al 1985 https://journals.aps.org/pra/abstract/10.1103/PhysRevA.32.10...

This was why Hopfield was awarded a physics Nobel - spin glasses as emergent learning.

Along with Expected Energy of Brownian motion in gases for Hinton’s Boltzmann Machines ( and of course backprop being Newtonian downhill on the manifold of correctness ).

Langland’s worthy crossovers and the utility of the generalisation.

the word is "probabilistic"