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Found 7 papers, 1 papers with code
Entropy-based Pruning for Learning Bayesian Networks using BIC
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize over this collection by choosing one parent set for each variable without creating directed cycles while maximizing the total score.
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
We discuss the computational complexity of approximating maximum a posteriori inference in sum-product networks.
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Learning Bayesian Networks with Incomplete Data by Augmentation
To the best of our knowledge, this is the first exact algorithm for this problem.
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables.
Learning Bayesian Networks with Thousands of Variables
We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints.
Global Sensitivity Analysis for MAP Inference in Graphical Models
We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters.
