com.bayesserver.inference

Class LoopyBeliefInference

java.lang.Object

com.bayesserver.inference.LoopyBeliefInference

All Implemented Interfaces:

Inference

public final class LoopyBeliefInference
extends Object
implements Inference

An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.

See Also:

Inference

Constructor Summary

Constructors

Constructor and Description
LoopyBeliefInference(Network network) Initializes a new instance of the LoopyBeliefInference class, with the target Bayesian network.

Method Summary

Modifier and Type	Method and Description
Evidence	getBaseEvidence() Optional evidence which can be used to calculate the lift of queries.
Evidence	getEvidence() Represents the evidence, or case data (e.g.
Network	getNetwork() The target Bayesian network.
QueryDistributionCollection	getQueryDistributions() Gets the collection of distributions to calculate.
QueryFunctionCollection	getQueryFunctions() Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
void	query(QueryOptions queryOptions, QueryOutput queryOutput) Calculates a number of distributions, e.g.
void	setBaseEvidence(Evidence value) Optional evidence which can be used to calculate the lift of queries.
void	setEvidence(Evidence value) Represents the evidence, or case data (e.g.
void	setQueryDistributions(QueryDistributionCollection value) Sets the collection of distributions to calculate.
void	setQueryFunctions(QueryFunctionCollection value) Sets the collection of functions to evaluate, after QueryDistributions have been calculated.

Methods inherited from class java.lang.Object

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

Constructor Detail

LoopyBeliefInference

public LoopyBeliefInference(Network network)

Initializes a new instance of the LoopyBeliefInference class, with the target Bayesian network.

Parameters:

network - The target Network.

Throws:

NullPointerException - Raised if [network] is null.

Method Detail

getNetwork

public Network getNetwork()

The target Bayesian network.

Specified by:

getNetwork in interface Inference

getEvidence

public Evidence getEvidence()

Represents the evidence, or case data (e.g. row in a database) used in a query. The distributions are only recalculated by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput), not each time evidence is changed.

For more information see Evidence.

Specified by:

getEvidence in interface Inference

setEvidence

public void setEvidence(Evidence value)

Represents the evidence, or case data (e.g. row in a database) used in a query. The distributions are only recalculated by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput), not each time evidence is changed.

For more information see Evidence.

Specified by:

setEvidence in interface Inference

getBaseEvidence

public Evidence getBaseEvidence()

Optional evidence which can be used to calculate the lift of queries. Defaults to null. When QueryDistribution.getComparison() is true queries are adjusted based on marginals calculated with this evidence or no evidence if this value is null.

Specified by:

getBaseEvidence in interface Inference

See Also:

QueryDistribution.getComparison()

setBaseEvidence

public void setBaseEvidence(Evidence value)

Optional evidence which can be used to calculate the lift of queries. Defaults to null. When QueryDistribution.getComparison() is true queries are adjusted based on marginals calculated with this evidence or no evidence if this value is null.

Specified by:

setBaseEvidence in interface Inference

See Also:

QueryDistribution.getComparison()

getQueryDistributions

public QueryDistributionCollection getQueryDistributions()

Gets the collection of distributions to calculate. See QueryDistributionCollection. If required, a query distribution collection can be dynamically attached or detached. Also note that individual elements can be enabled or disabled on a per query basis.

Specified by:

getQueryDistributions in interface Inference

setQueryDistributions

public void setQueryDistributions(QueryDistributionCollection value)

Sets the collection of distributions to calculate. See QueryDistributionCollection. If required, a query distribution collection can be dynamically attached or detached. Also note that individual elements can be enabled or disabled on a per query basis.

Specified by:

setQueryDistributions in interface Inference

getQueryFunctions

public QueryFunctionCollection getQueryFunctions()

Gets the collection of functions to evaluate, after QueryDistributions have been calculated. If required, a query function collection can be dynamically attached or detached. Also note that individual elements can be enabled or disabled on a per query basis.

Specified by:

getQueryFunctions in interface Inference

setQueryFunctions

public void setQueryFunctions(QueryFunctionCollection value)

Sets the collection of functions to evaluate, after QueryDistributions have been calculated. If required, a query function collection can be dynamically attached or detached. Also note that individual elements can be enabled or disabled on a per query basis.

Specified by:

setQueryFunctions in interface Inference

query

public void query(QueryOptions queryOptions,
                  QueryOutput queryOutput)
           throws InconsistentEvidenceException

Calculates a number of distributions, e.g. P(A) and P(B) given the evidence (case data, e.g. row in a database), and if requested the log-likelihood of the data. Each time this method is called the distributions have their values replaced, acting like buffers.

As well as requesting distributions of the form P(A), P(B) it is also possible to request distributions over a number of variables such as P(A,B).

If CLGaussian distributions are requested that include instantiated discrete variables, an algorithm may legitimately return mean and covariance values equal to NaN for entries corresponding to inconsistent discrete combinations. For example, if a CLGaussian is requested that includes a discrete variable 'Gender' with states 'Male' and 'Female' and evidence is set to 'Female' then any mean and covariance entries that correspond to 'Male' may be set to NaN. NaN values will always be accompanied by a corresponding zero value in the CLGaussian.getTable().

Specified by:

query in interface Inference

Parameters:

queryOptions - Options governing which calculations are performed and how.

queryOutput - Returns any information, in addition to the distributions, that is requested. For example the log-likelihood.

Throws:

InconsistentEvidenceException - Raised if inconsistent evidence is detected, or underflow/overflow occurs when calculating queries or the conflict measure. Note that log-likelihood calculations do not raise this exception, but instead report -Infinity or +Infinity.