Package edu.cmu.tetrad.bayes

Interface BayesUpdater

All Superinterfaces:

Serializable, TetradSerializable

All Known Subinterfaces:

ManipulatingBayesUpdater

All Known Implementing Classes:

ApproximateUpdater, CptInvariantUpdater, Identifiability, JunctionTreeUpdater, RowSummingExactUpdater

public interface BayesUpdater
extends TetradSerializable

Interface for a discrete Bayes updating algorithm. The main task of such and algorithm is to calculate P(X = x' | evidence), where evidence takes the form of a Proposition over the variables in the Bayes net, possibly with additional information about which variables in the Bayes net have been manipulated. Some updaters may be able to calculate joint marginals as well--that is, P(AND_i{Xi = xi'} | evidence). Also, not all updaters can take manipulation information into account. See implementations for details.)

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Evidence
• Proposition
• Manipulation

Method Summary

Modifier and Type	Method	Description
double[]	calculatePriorMarginals(int nodeIndex)	Calculates the prior marginal probabilities of the given node.
double[]	calculateUpdatedMarginals(int nodeIndex)	Calculates the updated marginal probabilities of the given node, given the evidence.
BayesIm	getBayesIm()	Returns the evidence for the updater.
double	getJointMarginal(int[] variables, int[] values)	Returns the joint marginal probability of the given variables taking the given values, given the evidence.
double	getMarginal(int variable, int category)	Returns the marginal probability of the given variable taking the given value, given the evidence.
boolean	isJointMarginalSupported()	Returns the joint marginal probability of the given variables taking the given values, given the evidence.
void	setEvidence(Evidence evidence)	Sets new evidence for the updater.

Method Details

getMarginal

double getMarginal(int variable,
 int category)

Returns the marginal probability of the given variable taking the given value, given the evidence.

Parameters:

variable - variable index

category - category index

Returns:

P(variable = value | evidence), where evidence is getEvidence().

isJointMarginalSupported

boolean isJointMarginalSupported()

Returns the joint marginal probability of the given variables taking the given values, given the evidence.

Returns:

true if the getJointMarginal() method is supported.

getJointMarginal

double getJointMarginal(int[] variables,
 int[] values)

Returns the joint marginal probability of the given variables taking the given values, given the evidence.

Parameters:

variables - variable indices

values - category indices

Returns:

P(variables[i] = values[i] | evidence), where evidence is getEvidence().

setEvidence

void setEvidence(Evidence evidence)

Sets new evidence for the updater. Once this is called, old updating results should not longer be available.

Parameters:

evidence - evidence

getBayesIm

BayesIm getBayesIm()

Returns the evidence for the updater.

Returns:

the Bayes instantiated model that is being updated.

calculatePriorMarginals

double[] calculatePriorMarginals(int nodeIndex)

Calculates the prior marginal probabilities of the given node.

Parameters:

nodeIndex - node index

Returns:

P(node = value), where value is the value of the node in the Bayes net.

calculateUpdatedMarginals

double[] calculateUpdatedMarginals(int nodeIndex)

Calculates the updated marginal probabilities of the given node, given the evidence.

Parameters:

nodeIndex - node index

Returns:

P(node = value | evidence), where value is the value of the node in the Bayes net.