DifferentiableStatisticalModels that are directed graphical models.See: Description
| Class | Description |
|---|---|
| BayesianNetworkDiffSM |
This class implements a scoring function that is a moral directed graphical
model, i.e.
|
| BayesianNetworkDiffSMParameterSet |
Class for the parameters of a
BayesianNetworkDiffSM. |
| BNDiffSMParameter |
Class for the parameters of a
BayesianNetworkDiffSM. |
| BNDiffSMParameterTree |
Class for the tree that represents the context of a
BNDiffSMParameter in a
BayesianNetworkDiffSM. |
| MarkovModelDiffSM |
This class implements a
AbstractDifferentiableStatisticalModel for an inhomogeneous Markov model. |
DifferentiableStatisticalModels that are directed graphical models. The currently implemented
BayesianNetworkDiffSM provides structure learning for inhomogeneous Markov models, Bayesian trees, and permuted Markov models,
but can be extended to other graph structures easily using the Measure interface.
It is important to define only moral Bayesian network structures because of the chosen parameterization.