1149 search results - page 1 / 230 » Learning Dynamic Bayesian Networks |
SARA
2007
Springer
13 years 10 months ago
2007
Springer
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...
GECCO
2003
Springer
13 years 9 months ago
2003
Springer
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
NN
1997
Springer
13 years 8 months ago
1997
Springer
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
ICALT
2008
IEEE
13 years 11 months ago
2008
IEEE
When using Learning Object Repositories, it is interesting to have mechanisms to select the more adequate objects for each student. For this kind of adaptation, it is important to...
JMLR
2010
12 years 11 months ago
2010
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...