aima.core.probability.bayes

Interface DynamicBayesianNetwork

All Superinterfaces:

BayesianNetwork

All Known Implementing Classes:

DynamicBayesNet

public interface DynamicBayesianNetwork
extends BayesianNetwork

Artificial Intelligence A Modern Approach (3rd Edition): page 590.

A dynamic Bayesian network, or DBN, is a Bayesian network that represents a temporal probability model. In general, each slice of a DBN can have any number of state variables X_{tEt. For simplicity, we assume that the variables and their links are exactly replicated from slice to slice and that the DBN represents a first-order Markov process, so that each variable can have parents only in its own slice or the immediately preceding slice.}

Author:

Ciaran O'Reilly

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 interface aima.core.probability.bayes.BayesianNetwork

getNode, getVariablesInTopologicalOrder

Method Detail

getPriorNetwork

BayesianNetwork getPriorNetwork()

Returns:

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

getX_0

java.util.Set<RandomVariable> getX_0()

Returns:

the set of state variables representing the prior distribution.

getX_1

java.util.Set<RandomVariable> getX_1()

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

java.util.List<RandomVariable> getX_1_VariablesInTopologicalOrder()

Returns:

the X_1 variables in topological order.

getX_0_to_X_1

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

Returns:

a Map indicating equivalent variables between X₀ and X₁.

getX_1_to_X_0

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

Returns:

a Map indicating equivalent variables between X₁ and X₀.

getE_1

java.util.Set<RandomVariable> getE_1()

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).