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Class Summary |
| BinaryProbDistribution |
This class implements the common features and data structures for the binary
probability distributions. |
| BivariateProbDistribution |
This class implements the common features and data structures of bivariate
probability distributions. |
| Chain |
Learns a bivariate model from bitChains where each node can have only one
child. |
| DependencyNetworkProbDistribution |
This abstract class implements common features that are needed for
multivariate probability distributions that allow cycles (dependency
networks) like Gibbs sampling. |
| MarginalProbabilityVector |
This class implements a probability distribution for binary elements
assuming that all variables are independents. |
| MarginalProducts |
This class stores the probabilities as marginal distributions of subsets
of variables as it is done in EcGA proposed by George Harik. |
| MultivariateMI_DN |
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| MultivariateModel |
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| Tree |
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