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.

该研究探讨了如何从动态过程的相关样本中重构二值图形模型，并分析了基于交互筛选目标和条件似然损失的两个估计器的样本复杂性。我们发现，对于来自远离平衡的动态过程的样本，样本复杂度与混合速度快的动态过程相比呈指数级下降。