Add to ORKGIn Bayesian networks, a most probable explanation (MPE) is a most likely instantiation of all network variables given a piece of evidence. Recent work proposed a branch-and-bound search algorithmthat finds exact solutions to MPE queries, where bounds ar
Finding the most probable explanation for observed variables in a Bayesian network is a notoriously ...
For hard computational problems, stochastic local search has proven to be a competitive approach to...
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely stu...
Most Relevant Explanation (MRE) is a new inference task in Bayesian networks that finds the most rel...
In addition to computing the posterior distributions for hidden variables in Bayesian networks, one ...
Finding most probable explanations (MPEs) in graphical models, such as Bayesian belief networks, is ...
The use of Bayesian networks has been shown to be powerful for supporting decision making, for examp...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
A major inference task in Bayesian networks is explaining why some variables are ob-served in their ...
Abstract—Abductive inference in Bayesian networks, is the problem of finding the most likely joint a...
Contains fulltext : 83894.pdf (publisher's version ) (Open Access)20 p1 p
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is t...
Abstract. This paper provides a study of the theoretical properties of Most Relevant Explanation (MR...
The paper describes a branch and bound scheme that uses heuristics generated mechanically by the min...
Abstract Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian beli...
Finding the most probable explanation for observed variables in a Bayesian network is a notoriously ...
For hard computational problems, stochastic local search has proven to be a competitive approach to...
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely stu...
Most Relevant Explanation (MRE) is a new inference task in Bayesian networks that finds the most rel...
In addition to computing the posterior distributions for hidden variables in Bayesian networks, one ...
Finding most probable explanations (MPEs) in graphical models, such as Bayesian belief networks, is ...
The use of Bayesian networks has been shown to be powerful for supporting decision making, for examp...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
A major inference task in Bayesian networks is explaining why some variables are ob-served in their ...
Abstract—Abductive inference in Bayesian networks, is the problem of finding the most likely joint a...
Contains fulltext : 83894.pdf (publisher's version ) (Open Access)20 p1 p
Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is t...
Abstract. This paper provides a study of the theoretical properties of Most Relevant Explanation (MR...
The paper describes a branch and bound scheme that uses heuristics generated mechanically by the min...
Abstract Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian beli...
Finding the most probable explanation for observed variables in a Bayesian network is a notoriously ...
For hard computational problems, stochastic local search has proven to be a competitive approach to...
Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely stu...