I’m trying to understand if I can use Stan in an inference problem I’m faced with, and would appreciate any feedback. Disclaimer - I’m relatively new to Bayesian methods in general, and very new to Stan and R in particular (used python so far).
The problem domain is 2D Ising models - maximum entropy models over binary variables, constrained by means and pairwise correlations. These distributions are parametrized by the Lagrange multipliers satisfying these constraints (the “fields” and “couplings” in stat. mech terms). I have samples from a given distribution, and I’m trying to use bayesian inference to estiamate the parameters of this distribution (which are continuous variables). I’ve played around with the IsingFit library, but as far as I can tell, this library:
- Does not provide an estimate of the uncertainty in the inferred values (ideally, I’d want to see “tighter” estimates given more data).
- Ultimately, I’m interested in representing the MaxEnt model parameters as functions of other variables, and putting priors on them - I don’t think this is supported by IsingFit.
It this possible to do with Stan? Any help would be much appreciated.