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Scaling laws for learning high-dimensional Markov forest distributions
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is structurally consistent and the error probability of structure learning decays faster than any polynomial in the …