I am a computer science master student. In a statistical learning theory course I am taking, mean field approximation was introduced to approximately solve non-factorizable Gibbs distributions that were derived using maximum entropy inference. Our professor has a strong background in physics and often uses terms from statistical physics. Unfortunately, I lack that background. So,
Are there any resources explaining mean field approximations from a non-physical/computer science perspective? I couldn't find any.
Or alternatively, are there "crash course"-like resources that would allow me to understand one of the more physically motivated explanations without looking up tons of terms?
EDIT: Here are some details that hopefully help to narrow it down: In this course, we are trying to sample from intractable, non-factorizable Gibbs-distributions (mainly in the context of clustering). Apparently, this cannot be done (efficiently), therefore we retreat to a mean-field approximation.