One way to think of it is that each point in your data follows your model but with gaussian iid noise shifting them away. The likelihood is then product of gaussians mean shifted and rescaled by variance. Minimize the log-likelihood then becomes reducing the sum of (x-mu)^2 for each point, which is essentially least squares.
My question is how much of the operations in JAX here can be done with reduced precision and can utilize training accelerators i.e. TPUs. I've noticed a lot of research coming out in physics, where everything is simulated in at least double float, being augmented with ML approaches where precision is traded for dynamic range.
A bit off-topic, but are there any Tailscale users here who consistently use it on mobile? How was the speed and battery life? I see that Tailscale offers a lot of nifty features like this that go beyond setting up a VPN, but if the overhead is too big it might not be worth running at all.