Chow and Liu show how to select second-order terms for the product approximation so that, among all such second-order approximations (first-order dependency trees), the constructed approximation P^{\prime} has the minimum Kullback-Leibler distance to the actual distribution P, and is thus the closest approximation in the classical information-theoretic sense.
Chow and Liu provide a simple algorithm for constructing the optimal tree; at each stage of the procedure the algorithm simply adds the maximum mutual information pair to the tree.