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 loglikelihood 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) . 
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  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) . 
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.

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.

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) . 
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.

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.

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  Description 

DecisionAlgorithm 
The type of algorithm to use when a network has decision nodes.

EvidenceType 
The type of evidence for a variable.

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  Description 

ConvergenceException 
Exception raised when an iterative inference algorithm fails to converge to within a given tolerance.

InconsistentEvidenceException 
Exception raised when either inconsistent evidence is detected, or underflow has occurred.

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