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Learning the sparse structure of a general Markov network is a hard computational problem. One of the main difficulties is the computation of the generally intractable partition function. To circumvent this difficulty, authors propose to learn the network structure using an ensemble-of-trees (ET) model. The ET model was first introduced by Meil?a and Jaakkola (2006), and it represents a multivariate distribution using a mixture of all possible spanning trees. The advantage of the ET model is that, although it needs to sum over super-exponentially many trees, its partition function as well as data likelihood can be computed in a closed form.
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