For a density composed of a Gaussian mixture model, mean-field theory can specify required model size and the size of the resulting approximation error.

“Uh just because kind of this have this loop of I make a prediction, then I go, I make an experiment. It I think is much more uh much more useful. And I think that the the big thing is that theory will never be able to explain literally anything, because uh it might be able to explain things on like a pure functional point of view. Uh let's say like the mean field people, they can completely explain if I have let's say uh a density composed of let's uh let's say a Gaussian mixture model.”

“Mortal composer of this thing. Then I can completely tell you, hey, you need this and that size a or like add this and that size the error to the infrared model will be this size.”