| Package | Description |
|---|---|
| de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels |
Provides
DifferentiableStatisticalModels that are directed graphical models. |
| de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures |
Provides the facilities to learn the structure of a
BayesianNetworkDiffSM. |
| de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.btMeasures |
Provides the facilities to learn the structure of a
BayesianNetworkDiffSM as
a Bayesian tree using a number of measures to define a rating of structures. |
| de.jstacs.sequenceScores.statisticalModels.differentiable.directedGraphicalModels.structureLearning.measures.pmmMeasures |
Provides the facilities to learn the structure of a
BayesianNetworkDiffSM as
a permuted Markov model using a number of measures to define a rating of structures. |
| Modifier and Type | Field and Description |
|---|---|
protected Measure |
BayesianNetworkDiffSM.structureMeasure
Measure that defines the network structure. |
| Modifier and Type | Method and Description |
|---|---|
Measure |
BayesianNetworkDiffSMParameterSet.getMeasure()
Returns the structure
Measure defined by this set of parameters. |
| Constructor and Description |
|---|
BayesianNetworkDiffSM(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new
BayesianNetworkDiffSM that has neither
been initialized nor trained. |
BayesianNetworkDiffSMParameterSet(AlphabetContainer alphabet,
int length,
double ess,
boolean plugInParameters,
Measure structureMeasure)
Creates a new
BayesianNetworkDiffSMParameterSet with
pre-defined parameter values. |
| Modifier and Type | Class and Description |
|---|---|
class |
InhomogeneousMarkov
Class for a network structure of a
BayesianNetworkDiffSM
that is an inhomogeneous Markov model. |
| Modifier and Type | Method and Description |
|---|---|
Measure |
Measure.clone() |
| Modifier and Type | Method and Description |
|---|---|
InstanceParameterSet<Measure> |
Measure.getCurrentParameterSet() |
| Constructor and Description |
|---|
MeasureParameterSet(Class<? extends Measure> clazz)
Creates a new empty
Measure.MeasureParameterSet for the given sub-class
of Measure, |
| Modifier and Type | Class and Description |
|---|---|
class |
BTExplainingAwayResidual
Structure learning
Measure that computes a maximum spanning tree
based on the explaining away residual and uses the resulting tree structure
as structure of a Bayesian tree (special case of a Bayesian network) in a
BayesianNetworkDiffSM
. |
class |
BTMutualInformation
Structure learning
Measure that computes a maximum spanning tree
based on mutual information and uses the resulting tree structure as
structure of a Bayesian tree (special case of a Bayesian network) in a
BayesianNetworkDiffSM
. |
| Modifier and Type | Class and Description |
|---|---|
class |
PMMExplainingAwayResidual
Class for the network structure of a
BayesianNetworkDiffSM
that is a permuted Markov model based on the explaining away residual. |
class |
PMMMutualInformation
Class for the network structure of a
BayesianNetworkDiffSM
that is a permuted Markov model based on mutual information. |