Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
A Linear-Time Algorithm for Finding Tree-Decompositions of Small Treewidth
SIAM Journal on Computing
An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
On the Desirability of Acyclic Database Schemes
Journal of the ACM (JACM)
Treewidth: Algorithmoc Techniques and Results
MFCS '97 Proceedings of the 22nd International Symposium on Mathematical Foundations of Computer Science
A Factor 2 Approximation Algorithm for the Generalized Steiner Network Problem
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Learning with mixtures of trees
Learning with mixtures of trees
A practical algorithm for finding optimal triangulations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Maximum likelihood bounded tree-width Markov networks
Artificial Intelligence
PAC-learning bounded tree-width graphical models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Graphical models of residue coupling in protein families
Proceedings of the 5th international workshop on Bioinformatics
Learning Factor Graphs in Polynomial Time and Sample Complexity
The Journal of Machine Learning Research
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Efficient and robust independence-based Markov network structure discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
Learning graphical models for hypothesis testing and classification
IEEE Transactions on Signal Processing
Maximum likelihood bounded tree-width Markov networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Phase transition of tractability in constraint satisfaction and bayesian network inference
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Multi-label classification using conditional dependency networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Approximate MRF inference using bounded treewidth subgraphs
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion
The Journal of Machine Learning Research
Parameterized complexity results for exact bayesian network structure learning
Journal of Artificial Intelligence Research
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Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a joint probability distribution. Once one has chosen to use a Markov network model, one aims to choose the model that “best explains” the data that has been observed—this model can then be used to make predictions about future data.We show that the problem of learning a maximum likelihood Markov network given certain observed data can be reduced to the problem of identifying a maximum weight low-treewidth graph under a given input weight function. We give the first constant factor approximation algorithm for this problem. More precisely, for any fixed treewidth objective k, we find a treewidth-k graph with an f(k) fraction of the maximum possible weight of any treewidth-k graph.