Uses of Interface
com.bayesserver.inference.Inference

Packages that use Inference

Package	Description
com.bayesserver.causal
com.bayesserver.inference

Uses of Inference in com.bayesserver.causal

Classes in com.bayesserver.causal that implement Inference

Modifier and Type	Class	Description
class	BackdoorInference	Estimates the causal effect, using the 'Backdoor Adjustment' formula to avoid confounding bias.
class	CausalInferenceBase	Base class for Causal inference engines used by internal algorithms.
class	DisjunctiveCauseInference	Estimates the causal effect, using the 'Disjunctive Cause Criterion' adjustment formula to avoid confounding bias.
class	FrontDoorInference	Estimates the causal effect, using the 'Front-door Adjustment' formula to avoid confounding bias.

Methods in com.bayesserver.causal that return Inference

Modifier and Type	Method	Description
Inference	BackdoorInferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.
Inference	DisjunctiveCauseInferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.
Inference	FrontDoorInferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.

Uses of Inference in com.bayesserver.inference

Classes in com.bayesserver.inference that implement Inference

Modifier and Type	Class	Description
class	LikelihoodSamplingInference	An approximate probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, based on Likelihood Sampling.
class	LoopyBeliefInference	An approximate but deterministic probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks based on Loopy Belief Propagation.
class	RelevanceTreeInference	An exact probabilistic inference algorithm for Bayesian networks and Dynamic Bayesian networks, that can compute multiple distributions more efficiently than the VariableEliminationInference algorithm.
class	VariableEliminationInference	An exact inference algorithm for Bayesian networks and Dynamic Bayesian networks, loosely based on the Variable Elimination algorithm.

Methods in com.bayesserver.inference that return Inference

Modifier and Type	Method	Description
Inference	InferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.
Inference	LikelihoodSamplingInferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.
Inference	LoopyBeliefInferenceFactory.createInferenceEngine(Network network)	Creates an instance of an inference algorithm, with the [network] as it's target.
Inference	RelevanceTreeInferenceFactory.createInferenceEngine(Network network)	Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
Inference	VariableEliminationInferenceFactory.createInferenceEngine(Network network)	Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
Inference	QueryLifecycleBegin.getInference()	The current inference engine.
Inference	QueryLifecycleBeginBase.getInference()	The current inference engine.
Inference	QueryLifecycleEnd.getInference()	The current inference engine.
Inference	QueryLifecycleEndBase.getInference()	The current inference engine.

Constructors in com.bayesserver.inference with parameters of type Inference

Constructor	Description
QueryLifecycleBeginBase(Inference inference, QueryOptions queryOptions)	For internal use.
QueryLifecycleEndBase(Inference inference, QueryOptions queryOptions, QueryOutput queryOutput)	For internal use.