As a demonstration of our method, we apply the learning rule to 10 different networks for sizes N = 4, 8, 16, 32, 64 assigning them means and correlations drawn at random from the distribution found for the 20 × 20 lattice Ising model. For each network, we apply Equation 3 for learning \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{i}^{\ast },{c}_{ij}^{\ast }$$\end{document}mi∗,cij∗, estimating the actual \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{i}^{m}$$\end{document}mim and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${c}_{ij}^{m}$$\end{document}cijm with Glauber dynamics.