aima.core.probability.bayes.impl

Class DynamicBayesNet

java.lang.Object

aima.core.probability.bayes.impl.BayesNet

aima.core.probability.bayes.impl.DynamicBayesNet

All Implemented Interfaces:

BayesianNetwork, DynamicBayesianNetwork

public class DynamicBayesNet
extends BayesNet
implements DynamicBayesianNetwork

Default implementation of the DynamicBayesianNetwork interface.

Author:

Ciaran O'Reilly

Field Summary

Fields inherited from class aima.core.probability.bayes.impl.BayesNet

rootNodes, variables, varToNodeMap

Constructor Summary

Constructors

Constructor and Description
DynamicBayesNet(BayesianNetwork priorNetwork, java.util.Map<RandomVariable,RandomVariable> X_0_to_X_1, java.util.Set<RandomVariable> E_1, Node... rootNodes)

Method Summary

Modifier and Type	Method and Description
java.util.Set<RandomVariable>	getE_1()
BayesianNetwork	getPriorNetwork()
java.util.Map<RandomVariable,RandomVariable>	getX_0_to_X_1()
java.util.Set<RandomVariable>	getX_0()
java.util.Map<RandomVariable,RandomVariable>	getX_1_to_X_0()
java.util.List<RandomVariable>	getX_1_VariablesInTopologicalOrder()
java.util.Set<RandomVariable>	getX_1()

Methods inherited from class aima.core.probability.bayes.impl.BayesNet

getNode, getVariablesInTopologicalOrder

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface aima.core.probability.bayes.BayesianNetwork

getNode, getVariablesInTopologicalOrder

Constructor Detail

DynamicBayesNet

public DynamicBayesNet(BayesianNetwork priorNetwork,
                       java.util.Map<RandomVariable,RandomVariable> X_0_to_X_1,
                       java.util.Set<RandomVariable> E_1,
                       Node... rootNodes)

Method Detail

getPriorNetwork

public BayesianNetwork getPriorNetwork()

Specified by:

getPriorNetwork in interface DynamicBayesianNetwork

Returns:

a Bayesian Network containing just the nodes representing the prior distribution (layer 0) of the dynamic bayesian network.

getX_0

public java.util.Set<RandomVariable> getX_0()

Specified by:

getX_0 in interface DynamicBayesianNetwork

Returns:

the set of state variables representing the prior distribution.

getX_1

public java.util.Set<RandomVariable> getX_1()

Specified by:

getX_1 in interface DynamicBayesianNetwork

Returns:

the set of state variables representing the first posterior slice of the DBN. This along with X₀ should represent the transition model P(X₁ | X₀).

getX_1_VariablesInTopologicalOrder

public java.util.List<RandomVariable> getX_1_VariablesInTopologicalOrder()

Specified by:

getX_1_VariablesInTopologicalOrder in interface DynamicBayesianNetwork

Returns:

the X_1 variables in topological order.

getX_0_to_X_1

public java.util.Map<RandomVariable,RandomVariable> getX_0_to_X_1()

Specified by:

getX_0_to_X_1 in interface DynamicBayesianNetwork

Returns:

a Map indicating equivalent variables between X₀ and X₁.

getX_1_to_X_0

public java.util.Map<RandomVariable,RandomVariable> getX_1_to_X_0()

Specified by:

getX_1_to_X_0 in interface DynamicBayesianNetwork

Returns:

a Map indicating equivalent variables between X₁ and X₀.

getE_1

public java.util.Set<RandomVariable> getE_1()

Specified by:

getE_1 in interface DynamicBayesianNetwork

Returns:

the set of state variables representing the evidence variables for the DBN. This along with X₁ should represent the sensor model P(E₁ | X₁).