An Analytic Solution to the Inverse Ising Problem in the Tree-reweighted Approximation
This provides a faster, accurate method for inferring interactions in systems like neural connectivity, though it is incremental as it builds on existing approximations.
The authors tackled the inverse Ising problem by deriving an analytic, non-iterative solution using the tree-reweighted approximation, which achieved the best estimates in strongly-attractive regions and matched gradient ascent results on real-world neuron spike data without iterative computation.
Many iterative and non-iterative methods have been developed for inverse problems associated with Ising models. Aiming to derive an accurate non-iterative method for the inverse problems, we employ the tree-reweighted approximation. Using the tree-reweighted approximation, we can optimize the rigorous lower bound of the objective function. By solving the moment-matching and self-consistency conditions analytically, we can derive the interaction matrix as a function of the given data statistics. With this solution, we can obtain the optimal interaction matrix without iterative computation. To evaluate the accuracy of the proposed inverse formula, we compared our results to those obtained by existing inverse formulae derived with other approximations. In an experiment to reconstruct the interaction matrix, we found that the proposed formula returns the best estimates in strongly-attractive regions for various graph structures. We also performed an experiment using real-world biological data. When applied to finding the connectivity of neurons from spike train data, the proposed formula gave the closest result to that obtained by a gradient ascent algorithm, which typically requires thousands of iterations.