Package | Description |
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com.bayesserver.inference |
Modifier and Type | Class and Description |
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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.
|
Modifier and Type | Method and Description |
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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.
|
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