AbstractWe 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
Graphical models are commonly used to encode conditional independence assumptions between random var...
International audienceWe show that the usual score function for conditional Markov networks can be w...
We show that the usual score function for conditional Markov networks can be written as the expectat...
We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution...
AbstractWe study the problem of projecting a distribution onto (or finding a maximum likelihood dist...
Markov trees generalize naturally to bounded tree-width Markov networks, onwhich exact computations ...
November 21, 2010The problem of maximum-likelihood (ML) estimation of discrete tree-structured distr...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth....
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact ...
Extended version of the ICML-2013 paper.International audienceWe consider the problem of learning th...
peer reviewedWe consider algorithms for generating Mixtures of Bagged Markov Trees, for density esti...
\u3cp\u3eThis work presents novel algorithms for learning Bayesian networks of bounded treewidth. Bo...
Graphical models are commonly used to encode conditional independence assumptions between random var...
International audienceWe show that the usual score function for conditional Markov networks can be w...
We show that the usual score function for conditional Markov networks can be written as the expectat...
We study the problem of projecting a distribution onto (or finding a maximum likelihood distribution...
AbstractWe study the problem of projecting a distribution onto (or finding a maximum likelihood dist...
Markov trees generalize naturally to bounded tree-width Markov networks, onwhich exact computations ...
November 21, 2010The problem of maximum-likelihood (ML) estimation of discrete tree-structured distr...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth....
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact ...
Extended version of the ICML-2013 paper.International audienceWe consider the problem of learning th...
peer reviewedWe consider algorithms for generating Mixtures of Bagged Markov Trees, for density esti...
\u3cp\u3eThis work presents novel algorithms for learning Bayesian networks of bounded treewidth. Bo...
Graphical models are commonly used to encode conditional independence assumptions between random var...
International audienceWe show that the usual score function for conditional Markov networks can be w...
We show that the usual score function for conditional Markov networks can be written as the expectat...
DOI
Add to ORKG