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We study analytically the performance of a recently proposed algorithm forlearning the couplings of a random asymmetric kinetic Ising model from finitelength trajectories of the spin dynamics. Our analysis shows the importance ofthe nontrivial equal time correlations between spins induced by the dynamicsfor the speed of learning. These correlations become more important as thespin's stochasticity is decreased. We also analyse the deviation of theestimation error from asymptotic optimality.

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves