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High Quality Content by WIKIPEDIA articles! A Chow-Liu tree is an efficient method for constructing a second-order product approximation of a joint distribution, first described in a paper by Chow & Liu (1968). The goals of such a decomposition, as with such Bayesian networks in general, may be either data compression or inference.Chow and Wagner proved in a later paper Chow & Wagner (1973) that the learning of the Chow-Liu tree is consistent given samples (or observations) drawn i.i.d. from a tree-structured distribution. In other words, the probability of learning an incorrect tree decays to zero as the number of samples tends to infinity. The main idea in the proof is the continuity of the mutual information in the pairwise marginal distribution. Recently, the exponential rate of convergence of the error probability was provided.