A B C D E F G H I J L M N P R S T U V

R

RandomVariable - Class in umbc.ebiquity.BayesOWL.commonDefine
This class implements a random variable in the classic discrete probability theory, which includes: (1) a name for this random variable (2) a set of possible states this random variable can take
RandomVariable(String, String[]) - Constructor for class umbc.ebiquity.BayesOWL.commonDefine.RandomVariable
Constructor.
RandomVariable(RandomVariable) - Constructor for class umbc.ebiquity.BayesOWL.commonDefine.RandomVariable
Constructor.
RetrieveLooseClosure - Class in umbc.ebiquity.BayesOWL.commonMethod
Given a set of random variables "Y = {V1, ..., Vn}" from a Bayesian Belief Network, retrieves the loose closure of "Y", which are defined as: "S = {Pi(V1), ..., Pi(Vn)}\Y".
RetrieveLooseClosure(String[], Net) - Constructor for class umbc.ebiquity.BayesOWL.commonMethod.RetrieveLooseClosure
Constructor.
RetrieveStrictClosure - Class in umbc.ebiquity.BayesOWL.commonMethod
Given a set of random variables "Y = {V1, ..., Vn}" from a Bayesian Belief Network, retrieves the strict closure of "Y", which are defined as:
Initially, "S = {Pi(V1), ..., Pi(Vn)}\Y";
If some "Si" in "S" is a descendant of some "Yi" in "Y", then:
(1) "Y = Y + {Si}",
(2) "S = S - {Si} + Pi(Si)\Y".
Repeat this process until such a "Si" does not exist any more.
RetrieveStrictClosure(String[], Net) - Constructor for class umbc.ebiquity.BayesOWL.commonMethod.RetrieveStrictClosure
Constructs with a given Bayesian Belief Network and a given set of random variables from this belief network.
run(int, double) - Method in class umbc.ebiquity.BayesOWL.constructor.CPTConstructor
Run reasoner.
run(int, double) - Method in class umbc.ebiquity.BayesOWL.coreAlgorithms.CIPFP
The loop process of conditional iterative proportional fitting procedure (CIPFP).
run(int, double) - Method in class umbc.ebiquity.BayesOWL.coreAlgorithms.DIPFP
Implements the main idea of the D-IPFP algorithm.
run(int, double) - Method in class umbc.ebiquity.BayesOWL.coreAlgorithms.EIPFP
Implements the main idea of the E-IPFP algorithm.
run(int, double) - Method in class umbc.ebiquity.BayesOWL.coreAlgorithms.IPFP
The loop process of iterative proportional fitting procedure (IPFP).
run(int, double) - Method in class umbc.ebiquity.BayesOWL.coreAlgorithms.SDIPFP
The loop process of simplified de-composed iterative proportional fitting procedure (SDIPFP).
run() - Method in class umbc.ebiquity.BayesOWL.GUI.MainUI
Start BayesOWL GUI.

A B C D E F G H I J L M N P R S T U V