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The methods of statistical physics are widely used for modelling complexnetworks. Building on the recently proposed Equilibrium Expectation approach,we derive a simple and efficient algorithm for maximum likelihood estimation(MLE) of parameters of exponential family distributions - a family ofstatistical models, that includes Ising model, Markov Random Field andExponential Random Graph models. Computational experiments and analysis ofempirical data demonstrate that the algorithm increases by orders of magnitudethe size of network data amenable to Monte Carlo based inference. We reportresults suggesting that the applicability of the algorithm may readily beextended to the analysis of large samples of dependent observations commonlyfound in biology, sociology, astrophysics, and ecology.

A Simple Algorithm for Scalable Monte Carlo Inference