Uses of Class
edu.cmu.tetrad.bayes.BayesPm

Packages that use BayesPm

Package	Description
edu.cmu.tetrad.algcomparison.simulation
edu.cmu.tetrad.bayes

Uses of BayesPm in edu.cmu.tetrad.algcomparison.simulation

Constructors in edu.cmu.tetrad.algcomparison.simulation with parameters of type BayesPm

Modifier	Constructor	Description
	BayesNetSimulation(BayesPm pm)	Constructs a new BayesNetSimulation.

Uses of BayesPm in edu.cmu.tetrad.bayes

Methods in edu.cmu.tetrad.bayes that return BayesPm

Modifier and Type	Method	Description
BayesPm	BayesIm.getBayesPm()	Returns the underlying Bayes PM.
BayesPm	DirichletBayesIm.getBayesPm()	Getter for the field bayesPm.
BayesPm	EmBayesProperties.getBayesPm()	Getter for the field bayesPm.
BayesPm	MlBayesIm.getBayesPm()	Getter for the field bayesPm.
BayesPm	MlBayesImObs.getBayesPm()	Getter for the field bayesPm.
BayesPm	UpdatedBayesIm.getBayesPm()	getBayesPm.
static BayesPm	BayesPm.serializableInstance()	Generates a simple exemplar of this class to test serialization.

Methods in edu.cmu.tetrad.bayes with parameters of type BayesPm

Modifier and Type	Method	Description
static DirichletBayesIm	DirichletBayesIm.blankDirichletIm(BayesPm bayesPm)	blankDirichletIm.
BayesIm	EmBayesProperties.Estimator.estimate(BayesPm bayesPm, DataSet dataSet)	estimate.
BayesIm	MlBayesEstimator.estimate(BayesPm bayesPm, DataSet dataSet)	Estimates parameters of the given Bayes net from the given data using maximum likelihood method.
BayesIm	MlBayesEstimatorOld.estimate(BayesPm bayesPm, DataSet dataSet)	33 Estimates a Bayes IM using the variables, graph, and parameters in the given Bayes PM and the data columns in the given data set.
double[][]	BdeMetricCache.getObservedCounts(Node node, BayesPm bayesPm, BayesIm bayesIm)	This method is used in testing and debugging and not in the BDe metric calculations.
double	BdeMetricCache.scoreLnGam(Node node, Set<Node> parents, BayesPm bayesPmMod, BayesIm bayesIm)	Computes the BDe score, using the logarithm of the gamma function, relative to the data, of the factor determined by a node and its parents.
static DirichletBayesIm	DirichletBayesIm.symmetricDirichletIm(BayesPm bayesPm, double symmetricAlpha)	symmetricDirichletIm.

Constructors in edu.cmu.tetrad.bayes with parameters of type BayesPm

Modifier	Constructor	Description
	BayesPm(BayesPm bayesPm)	Copy constructor.
	BayesPm(Graph graph, BayesPm oldBayesPm)	Constructs a new BayesPm using a given DAG, using as much information from the old BayesPm as possible.
	BayesPm(Graph graph, BayesPm oldBayesPm, int lowerBound, int upperBound)	Constructs a new BayesPm from the given DAG, using as much information from the old BayesPm as possible.
	BdeMetricCache(DataSet dataSet, BayesPm bayesPm)	Constructs a BdeMetricCache object for a given dataset and BayesPm.
	EmBayesEstimator(BayesPm bayesPm, DataSet dataSet)	Provides methods for estimating a Bayes IM from an existing BayesIM and a discrete dataset using EM (Expectation Maximization).
	FactoredBayesStructuralEM(DataSet dataSet, BayesPm bayesPmM0)	Constructor for FactoredBayesStructuralEM.
	MlBayesIm(BayesPm bayesPm)	Constructs a new BayesIm from the given BayesPm, initializing all values as Double.NaN ("?").
	MlBayesIm(BayesPm bayesPm, boolean countsOnly)	Constructs an instance of MlBayesIm.
	MlBayesIm(BayesPm bayesPm, BayesIm oldBayesIm, MlBayesIm.InitializationMethod initializationMethod)	Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM, but using values from the old BayesIm provided where posssible.
	MlBayesIm(BayesPm bayesPm, MlBayesIm.InitializationMethod initializationMethod)	Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM.
	MlBayesImObs(BayesPm bayesPm)	Constructs a new BayesIm from the given BayesPm, initializing all values as Double.NaN ("?").
	MlBayesImObs(BayesPm bayesPm, int initializationMethod)	Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM.
	MlBayesImObs(BayesPm bayesPm, BayesIm oldBayesIm, int initializationMethod)	Constructs a new BayesIm from the given BayesPm, initializing values either as MANUAL or RANDOM, but using values from the old BayesIm provided where posssible.