AbstractDirected-path (DP) singly-connected Bayesian networks are an interesting special case that, in particular, includes both polytrees and two-level networks. We analyze the computational complexity of these networks. The prediction problem is shown to be easy, as standard message passing can perform correct updating. However, diagnostic reasoning is hard even for DP singly-connected networks. In addition, finding the most-probable explanation (MPE) is hard, even without evidence. Finally, complexity of nearly DP singly-connected networks is analyzed
MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MA...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Baye...
AbstractDirected-path (DP) singly-connected Bayesian networks are an interesting special case that, ...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
Abstract Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian beli...
Multi-dimensional Bayesian networks (MBCs) have been recently shown to perform efficient classificat...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesia...
We examine the inferential complexity of Bayesian networks specified through logical constructs. We ...
In this thesis, the computational complexity of a number of problems related to probabilistic networ...
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Baye...
Reasoning with a Bayesian network amounts to computing probability distri-butions for the network’s ...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
We study the computational complexity of finding maximum a posteriori configurations in Bayesian net...
MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MA...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Baye...
AbstractDirected-path (DP) singly-connected Bayesian networks are an interesting special case that, ...
AbstractOne of the key computational problems in Bayesian networks is computing the maximal posterio...
Abstract Finding the I Most Probable IJxplanations (MPE) of a given evidence, Se, in a Bayesian beli...
Multi-dimensional Bayesian networks (MBCs) have been recently shown to perform efficient classificat...
\u3cp\u3eCredal networks are graph-based statistical models whose parameters take values in a set, i...
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesia...
We examine the inferential complexity of Bayesian networks specified through logical constructs. We ...
In this thesis, the computational complexity of a number of problems related to probabilistic networ...
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Baye...
Reasoning with a Bayesian network amounts to computing probability distri-butions for the network’s ...
Credal networks are graph-based statistical models whose parameters take values in a set, instead of...
We study the computational complexity of finding maximum a posteriori configurations in Bayesian net...
MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MA...
\u3cp\u3eThis paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bay...
Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Baye...