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• W2142496058 abstract "We consider unsupervised estimation of mixtures of discrete graphical models, where the class variable is hidden and each mixture component can have a potentially different Markov graph structure and parameters over the observed variables. We propose a novel method for estimating the mixture components with provable guarantees. Our output is a tree-mixture model which serves as a good approximation to the underlying graphical model mixture. The sample and computational requirements for our method scale as poly(p, r), for an r-component mixture of p-variate graphical models, for a wide class of models which includes tree mixtures and mixtures over bounded degree graphs." @default.
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• W2142496058 date "2012-12-03" @default.
• W2142496058 modified "2023-09-23" @default.
• W2142496058 title "Learning Mixtures of Tree Graphical Models" @default.
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