aima.core.probability.bayes

Interface Node

All Known Subinterfaces:

ContinuousNode, DiscreteNode, FiniteNode

All Known Implementing Classes:

AbstractNode, FullCPTNode

public interface Node

Artificial Intelligence A Modern Approach (3rd Edition): page 511.

A node is annotated with quantitative probability information. Each node corresponds to a random variable, which may be discrete or continuous. If there is an arrow from node X to node Y in a Bayesian Network, X is said to be a parent of Y and Y is a child of X. Each node X_i has a conditional probability distribution P(X_i | Parents(X_i)) that quantifies the effect of the parents on the node.

Author:

Ciaran O'Reilly

Method Summary

Modifier and Type	Method and Description
java.util.Set<Node>	getChildren()
ConditionalProbabilityDistribution	getCPD()
java.util.Set<Node>	getMarkovBlanket() Get this Node's Markov Blanket: 'A node is conditionally independent of all other nodes in the network, given its parents, children, and children's parents - that is, given its MARKOV BLANKET (AIMA3e pg, 517).
java.util.Set<Node>	getParents()
RandomVariable	getRandomVariable()
boolean	isRoot()

Method Detail

getRandomVariable

RandomVariable getRandomVariable()

Returns:

the Random Variable this Node is for/on.

isRoot

boolean isRoot()

Returns:

true if this Node has no parents.

See Also:

getParents()

getParents

java.util.Set<Node> getParents()

Returns:

the parent Nodes for this Node.

getChildren

java.util.Set<Node> getChildren()

Returns:

the children Nodes for this Node.

getMarkovBlanket

java.util.Set<Node> getMarkovBlanket()

Get this Node's Markov Blanket:
'A node is conditionally independent of all other nodes in the network, given its parents, children, and children's parents - that is, given its MARKOV BLANKET (AIMA3e pg, 517).

Returns:

this Node's Markov Blanket.

getCPD

ConditionalProbabilityDistribution getCPD()

Returns:

the Conditional Probability Distribution associated with this Node.