  The usual setting for learning the structure and parameters of a graphical
model assumes the availability of independent samples produced from the
corresponding multivariate probability distribution. However, for many models
the 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 binary
graphical models from correlated samples produced by a dynamical process, which
is natural in many applications. We analyze the sample complexity of two
estimators that are based on the interaction screening objective and the
conditional likelihood loss. We observe that for samples coming from a
dynamical process far from equilibrium, the sample complexity reduces
exponentially compared to a dynamical process that mixes quickly.
