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com.cra.figaro.algorithm.learning

EMWithImportance

object EMWithImportance

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. def apply(terminationCriteria: () ⇒ EMTerminationCriteria, importanceParticles: Int, params: ModelParameters)(implicit universe: Universe): GeneralizedEM

    An expectation maximization algorithm using importance sampling for inference.

    An expectation maximization algorithm using importance sampling for inference.

    terminationCriteria

    criteria for stopping the EM algorithm

    importanceParticles

    number of particles of the importance sampling algorithm

    params

    parameters to target with EM algorithm

  5. def apply(emIterations: Int, importanceParticles: Int, params: ModelParameters)(implicit universe: Universe): GeneralizedEM

    An expectation maximization algorithm using importance sampling for inference.

    An expectation maximization algorithm using importance sampling for inference.

    emIterations

    number of iterations of the EM algorithm

    importanceParticles

    number of particles of the importance sampling algorithm

    params

    parameters to target with EM algorithm

  6. def apply(params: ModelParameters)(implicit universe: Universe): GeneralizedEM

    An expectation maximization algorithm using importance sampling for inference.

    An expectation maximization algorithm using importance sampling for inference.

    params

    parameters to target with EM algorithm

  7. def apply(terminationCriteria: () ⇒ EMTerminationCriteria, importanceParticles: Int, p: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

    An expectation maximization algorithm using importance sampling for inference.

    An expectation maximization algorithm using importance sampling for inference.

    terminationCriteria

    criteria for stopping the EM algorithm

    importanceParticles

    number of particles of the importance sampling algorithm

  8. def apply(emIterations: Int, importanceParticles: Int, p: Parameter[_]*)(implicit universe: Universe): GeneralizedEM

    An expectation maximization algorithm using importance sampling for inference.

    An expectation maximization algorithm using importance sampling for inference.

    emIterations

    number of iterations of the EM algorithm

    importanceParticles

    number of particles of the importance sampling algorithm

  9. final def asInstanceOf[T0]: T0
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  13. def finalize(): Unit
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  14. final def getClass(): Class[_]
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  16. final def isInstanceOf[T0]: Boolean
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  18. final def notify(): Unit
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  19. final def notifyAll(): Unit
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  20. def online(transition: () ⇒ Universe, p: ModelParameters)(implicit universe: Universe): GeneralizedOnlineEM
  21. def online(transition: () ⇒ Universe, p: Parameter[_]*)(implicit universe: Universe): GeneralizedOnlineEM
  22. final def synchronized[T0](arg0: ⇒ T0): T0
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  26. final def wait(arg0: Long): Unit
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