I'm not really clear on the difference between Bayesian methods and EBMs. EBMs associate an "energy" to each point the the parameter space, but this energy is just the log-partition function of some distribution. Sampling schemes for such models are just MCMC methods, which are already a long-established genre of Bayesian techniques.<p>The article talks about sampling from EBMs using Langevin Dynamics, but it appears to be identical to Bayesian sampling with Langevin dynamics, which has been fairly popular for a few years. Some of the other stuff is just focused on minimizing the EBM, but then that's just identical to MAP/frequentist estimates.<p>Also, they gloss over a lot of problems that Langevin dynamics has. Unlike what they claim, it is not at all good at finding nodes separated by low-probability regions, since it has to take increasingly small steps to maintain asymtotic correctness.