aima.core.probability

Interface FiniteProbabilityModel

All Superinterfaces:

ProbabilityModel

All Known Implementing Classes:

FiniteBayesModel, FullJointDistributionBurglaryAlarmModel, FullJointDistributionMeningitisStiffNeckModel, FullJointDistributionModel, FullJointDistributionPairFairDiceModel, FullJointDistributionToothacheCavityCatchModel, FullJointDistributionToothacheCavityCatchWeatherModel

public interface FiniteProbabilityModel
extends ProbabilityModel

Artificial Intelligence A Modern Approach (3rd Edition): page 484.

A probability model on a discrete, countable set of worlds. The proper treatment of the continuous case brings in certain complications that are less relevant for most purposes in AI.

Author:

Ciaran O'Reilly

Field Summary

Fields inherited from interface aima.core.probability.ProbabilityModel

DEFAULT_ROUNDING_THRESHOLD

Method Summary

Modifier and Type	Method and Description
CategoricalDistribution	jointDistribution(Proposition... propositions) Get a distribution on multiple variables.
CategoricalDistribution	posteriorDistribution(Proposition phi, Proposition... evidence) Get a conditional distribution.
CategoricalDistribution	priorDistribution(Proposition... phi) P(X,...)

Methods inherited from interface aima.core.probability.ProbabilityModel

getRepresentation, isValid, posterior, prior

Method Detail

priorDistribution

CategoricalDistribution priorDistribution(Proposition... phi)

P(X,...)

Parameters:

phi - the propositions of interest.

Returns:

all the possible values of the propositions φ. This is a Vector of numbers, where we assume a predefined ordering of the domain of the relevant random variables.

posteriorDistribution

CategoricalDistribution posteriorDistribution(Proposition phi,
                                              Proposition... evidence)

Get a conditional distribution. Example:

P(X | Y) gives the values of P(X = x_i | Y = y_j) for each possible i, j pair.

Parameters:

phi - the proposition for which a probability distribution is to be returned.

evidence - information we already have.

Returns:

the conditional distribution for P(φ | evidence).

jointDistribution

CategoricalDistribution jointDistribution(Proposition... propositions)

Get a distribution on multiple variables. Example, the product rule:

P(X, Y) gives the values of P(X = x_i | Y = y_j)P(Y = y_j) for each possible i, j pair.

Parameters:

propositions - the propositions for which a joint probability distribution is to be returned.

Returns:

the joint distribution for P(X, Y, ...).