Add to ORKGWe 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...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
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...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
Abstract — The max-product “belief propagation ” algorithm is an iterative, local, message passing a...
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 ...
The belief propagation (BP) algorithm is a message-passing algorithm that is used for solving probab...
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...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
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...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
Abstract—The problem of maximum-likelihood (ML) estima-tion of discrete tree-structured distribution...
In this paper, we study the problem of learning the structure of Markov Networks that permit ecient ...
Abstract — The max-product “belief propagation ” algorithm is an iterative, local, message passing a...
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 ...
The belief propagation (BP) algorithm is a message-passing algorithm that is used for solving probab...
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...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...