Learning Markov networks: maximum bounded tree-width graphs
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Learning with mixtures of trees
Learning with mixtures of trees
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Fully dynamic algorithm for chordal graphs with O(1) query-time and O(n 2) update-time
Theoretical Computer Science
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We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution among) Markov networks of bounded tree-width. By casting it as the combinatorial optimization problem of finding a maximum weight hypertree, we prove that it is NP-hard to solve exactly and provide an approximation algorithm with a provable performance guarantee.