Package com.bayesserver.inference

Interface Summary

Interface	Description
Evidence	Represents the evidence, or case data (e.g.
Inference	The interface for a Bayesian network inference algorithm, which is used to perform queries such as calculating posterior probabilities and log-likelihood values for a case.
InferenceFactory	Uses the factory design pattern to create inference related objects for inference algorithms.
QueryDistributionCollection	The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryFunctionCollection	Collection of functions to be evaluated at query time, after any query distributions have been calculated.
QueryLifecycle	Allows callers to hook into the query lifecycle of an inference engine.
QueryLifecycleBegin	Contains information that is passed via the QueryLifecycle interface.
QueryLifecycleEnd	Contains information that is passed via the QueryLifecycle interface.
QueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
QuerySamplingOptions	Interface for approximate sampling inference algorithms, which can be implemented in addition to QueryOptions.

Class Summary

Class	Description
DefaultEvidence	Represents the evidence, or case data (e.g.
DefaultQueryDistributionCollection	The collection of distributions to be calculated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
DefaultQueryFunctionCollection	The collection of functions to be evaluated by a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
EvidenceTypes	Provides information about the type of evidence on a variable as well as whether it is an intervention (do operator) or not.
LikelihoodSamplingInference	An approximate probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, based on Likelihood Sampling.
LikelihoodSamplingInferenceFactory	Uses the factory design pattern to create inference related objects for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryLifecycleBegin	Query lifecycle begin implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryLifecycleEnd	Query end lifecycle implementation for the Likelihood Sampling algorithm.
LikelihoodSamplingQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LikelihoodSamplingQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
LoopyBeliefInference	An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.
LoopyBeliefInferenceFactory	Uses the factory design pattern to create inference related objects for the Loopy Belief algorithm.
LoopyBeliefQueryLifecycleBegin	Query lifecycle begin implementation for the Loopy Belief algorithm.
LoopyBeliefQueryLifecycleEnd	Query end lifecycle implementation for the Loopy Belief algorithm.
LoopyBeliefQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
LoopyBeliefQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
QueryDistribution	Defines a distribution to be queried in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryFunction	Defines a function to be evaluated in a call to Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
QueryFunctionOutput	A class whose value holds the result of a function evaluation, populated during a query.
QueryLifecycleBeginBase	Query begin lifecycle base class implementation for causal algorithms.
QueryLifecycleEndBase	Query end lifecycle base class implementation for causal algorithms.
RelevanceTreeInference	An exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
RelevanceTreeInferenceFactory	Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
RelevanceTreeQueryLifecycleBegin	Query lifecycle begin implementation for the Relevance Tree algorithm.
RelevanceTreeQueryLifecycleEnd	Query end lifecycle implementation for the Relevance Tree algorithm.
RelevanceTreeQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
RelevanceTreeQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.
SoftEvidence	Helper methods for manipulating soft/virtual evidence.
TreeQuery	Contains methods to determine properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
TreeQueryOptions	Options which affect the calculation performed by a TreeQuery.
TreeQueryOutput	Contains information output by a TreeQuery.
VariableEliminationInference	An exact inference algorithm for Bayesian networks and Dynamic Bayesian networks, loosely based on the Variable Elimination algorithm.
VariableEliminationInferenceFactory	Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
VariableEliminationQueryLifecycleBegin	Query lifecycle begin implementation for the Variable Elimination algorithm.
VariableEliminationQueryLifecycleEnd	Query end lifecycle implementation for the Variable Elimination algorithm.
VariableEliminationQueryOptions	Options that govern the calculations performed by Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput).
VariableEliminationQueryOutput	Returns any information, in addition to the distributions, that is requested from a query.

Enum Summary

Enum	Description
CausalEffectKind	The type of causal effect to identify or estimate.
DecisionAlgorithm	The type of algorithm to use when a network has decision nodes.
EvidenceType	The type of evidence for a variable.
InconsistentEvidenceMode	Determines when an InconsistentEvidenceException is raied.
InterventionType	Determines whether evidence is an intervention (do operator) or not.
QueryComparison	Determines whether and how queried values (e.g.
QueryDistance	Type of distance to calculate for a query.
QueryEvidenceMode	Determines how predictions on variables with evidence are performed.

Exception Summary

Exception	Description
ConvergenceException	Exception raised when an iterative inference algorithm fails to converge to within a given tolerance.
FunctionException	Exception raised during the evaluation of a function expression.
InconsistentEvidenceException	Exception raised when either inconsistent evidence is detected, or underflow has occurred.