Add to ORKGThis paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) prob-lem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.
AbstractProbabilistic inference and maximum a posteriori (MAP) explanation are two important and rel...
Learning optimal Bayesian networks (BN) from data is NP-hard in general. Nevertheless, certain BN cl...
We study the problem of learning the best Bayesian network structure with respect to a decomposable ...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
We study the computational complexity of finding maximum a posteriori configurations in Bayesian net...
AbstractFinding maximum a posteriori (MAP) assignments, also called Most Probable Explanations, is a...
MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MA...
The problem of finding the most probable explanation to a designated set of variables given partial ...
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely st...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesia...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
AbstractThis article describes an algorithm that solves the problem of finding the K most probable c...
AbstractProbabilistic inference and maximum a posteriori (MAP) explanation are two important and rel...
Learning optimal Bayesian networks (BN) from data is NP-hard in general. Nevertheless, certain BN cl...
We study the problem of learning the best Bayesian network structure with respect to a decomposable ...
This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem i...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian net...
We study the computational complexity of finding maximum a posteriori configurations in Bayesian net...
AbstractFinding maximum a posteriori (MAP) assignments, also called Most Probable Explanations, is a...
MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MA...
The problem of finding the most probable explanation to a designated set of variables given partial ...
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely st...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesia...
The problem of finding the most probable explanation to a designated set of vari-ables given partial...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
AbstractThis article describes an algorithm that solves the problem of finding the K most probable c...
AbstractProbabilistic inference and maximum a posteriori (MAP) explanation are two important and rel...
Learning optimal Bayesian networks (BN) from data is NP-hard in general. Nevertheless, certain BN cl...
We study the problem of learning the best Bayesian network structure with respect to a decomposable ...