106 search results - page 11 / 22 » The max-min hill-climbing Bayesian network structure learnin... |
97
Voted
ICML
2009
IEEE
16 years 14 days ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
79
Voted
IWINAC
2007
Springer
15 years 5 months ago
2007
Springer
One of the key points in Estimation of Distribution Algorithms (EDAs) is the learning of the probabilistic graphical model used to guide the search: the richer the model the more ...
103
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JMLR
2010
14 years 6 months ago
2010
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
94
Voted
ICMLA
2007
15 years 1 months ago
2007
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branch...
86
Voted
UAI
2004
15 years 1 months ago
2004
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...