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
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact ...
In general, the problem of computing a maximum a posteriori (MAP) assignment in a Markov random fiel...
Let S(υ) be a function defined on the vertices υ of the infinite binary tree. One algorithm to seek ...
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 ...
The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is consi...
We develop and analyze methods for computing provably optimal maximum a posteriori (MAP) configurati...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth....
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) prob-lem ...
Abstract — The max-product “belief propagation ” algorithm is an iterative, local, message passing a...
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact ...
In general, the problem of computing a maximum a posteriori (MAP) assignment in a Markov random fiel...
Let S(υ) be a function defined on the vertices υ of the infinite binary tree. One algorithm to seek ...
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 ...
The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is consi...
We develop and analyze methods for computing provably optimal maximum a posteriori (MAP) configurati...
In many applications one wants to compute conditional probabilities given a Bayesian network. This i...
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
This work presents novel algorithms for learning Bayesian network structures with bounded treewidth....
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) prob-lem ...
Abstract — The max-product “belief propagation ” algorithm is an iterative, local, message passing a...
This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact ...
In general, the problem of computing a maximum a posteriori (MAP) assignment in a Markov random fiel...
Let S(υ) be a function defined on the vertices υ of the infinite binary tree. One algorithm to seek ...
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