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The usual setting for learning the structure and parameters of a graphicalmodel assumes the availability of independent samples produced from thecorresponding multivariate probability distribution. However, for many modelsthe mixing time of the respective Markov chain can be very large and i.i.d.samples may not be obtained. We study the problem of reconstructing binarygraphical models from correlated samples produced by a dynamical process, whichis natural in many applications. We analyze the sample complexity of twoestimators that are based on the interaction screening objective and theconditional likelihood loss. We observe that for samples coming from adynamical process far from equilibrium, the sample complexity reducesexponentially compared to a dynamical process that mixes quickly.

Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics