  Maximum Likelihood Estimation (MLE) is the bread and butter of system
inference for stochastic systems. In some generality, MLE will converge to the
correct model in the infinite data limit. In the context of physical approaches
to system inference, such as Boltzmann machines, MLE requires the arduous
computation of partition functions summing over all configurations, both
observed and unobserved. We present here a conceptually and computationally
transparent data-driven approach to system inference that is based on the
simple question: How should the Boltzmann weights of observed configurations be
modified to make the probability distribution of observed configurations close
to a flat distribution? This algorithm gives accurate inference by using only
observed configurations for systems with a large number of degrees of freedom
where other approaches are intractable.
