Sparsistent Estimation of Time-Varying Discrete Markov Random Fields

We study a nonparametric method that estimates the structure of a discrete undirected graphical model from data. We assume that the distribution generating the data smoothly evolves over time and that the given sample is not identically distributed. Under the assumption that the underlying graphical model is sparse, the method recovers the structure consistently in the high dimensional, low sample size setting.