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

Abstract

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

17 pages and 4 figures

References in corpus (4)

• Mean Field Theory For Non-Equilibrium Network Reconstruction
• Dynamical TAP equations for non-equilibrium Ising spin glasses
• Belief-Propagation and replicas for inference and learning in a kinetic Ising model with hidden spins
• Inferring hidden states in a random kinetic Ising model: replica analysis

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