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BNDiffSMParameterTree.
Storable.
Storable.
Storable.
Storable.
HMMTrainingParameterSet for the Baum-Welch training of an AbstractHMM.Storable.
BayesianNetworkDiffSM that has neither
been initialized nor trained.
BayesianNetworkDiffSM that has neither
been initialized nor trained from a
BayesianNetworkDiffSMParameterSet.
Storable.
BayesianNetworkDiffSM.BayesianNetworkDiffSMParameterSet with
pre-defined parameter values.
BayesianNetworkDiffSMParameterSet with
empty parameter values.
BayesianNetworkDiffSMParameterSet from its
XML representation as defined by the Storable
interface.
StructureLearner.ModelType.BN ) of fixed order.BayesianNetworkTrainSM from a given
BayesianNetworkTrainSMParameterSet.
Storable.
ParameterSet for the class
BayesianNetworkTrainSM.Storable.
BayesianNetworkTrainSMParameterSet for a
BayesianNetworkTrainSM.
BayesianNetworkTrainSMParameterSet for a
BayesianNetworkTrainSM.
SequenceIterator, Sequence) to
DataSets and vice versa.BayesianNetworkDiffSM.BNDiffSMParameter, that is BNDiffSMParameter no
index in the list of BNDiffSMParameters of the
BayesianNetworkDiffSM and responsible for
symbol at position position and pseudo count
pseudoCount.
BNDiffSMParameter, that is BNDiffSMParameter no
index in the list of BNDiffSMParameters of the
BayesianNetworkDiffSM and responsible for
symbol at position position having context
context and pseudocount pseudoCount.
Storable.
BNDiffSMParameter in a
BayesianNetworkDiffSM.BNDiffSMParameterTree for the parameters at position
pos using the parent positions in contextPoss.
BNDiffSMParameterTree from its XML representation as
returned by BNDiffSMParameterTree.toXML().
BNDiffSMParameterTreeStorable interface.
[lower,upper].
[lower,upper].
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
.Measure.
BTExplainingAwayResidual from the corresponding
InstanceParameterSet parameters.
Storable.
BTExplainingAwayResidual structure
Measure.BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet with empty
parameter values.
BTExplainingAwayResidual.BTExplainingAwayResidualParameterSet with the
parameter for the equivalent sample sizes set to ess.
Storable
.
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
.Storable.
Measure.
BTMutualInformation from the corresponding
InstanceParameterSet parameters.
BTMutualInformation structure
Measure.BTMutualInformation.BTMutualInformationParameterSet with empty
parameter values.
BTMutualInformation.BTMutualInformationParameterSet with the
parameter for the BTMutualInformation.DataSource set to clazz and
the parameter for the equivalent sample sizes (ess) set to
ess.
Storable
.
Enum defining the possible sources of data to compute the mutual
information.BurnInTest, may be null for no test
BurnInTest that is used to stop the sampling.
bytes and can therefore be used for discrete
AlphabetContainers with alphabets that use only few symbols.ByteSequence from an array of byte-
encoded alphabet symbols.
ByteSequence from a String representation
using the default delimiter.
ByteSequence from a String representation
using the delimiter delim.
ByteSequence from a SymbolExtractor.
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