AbstractGPClassification

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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. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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8. def finalize(): Unit

   

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9. final def getClass(): Class[_]

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def logLikelihood(trainingData: DenseVector[Double], kernelMatrix: DenseMatrix[Double], f: DenseVector[Double], likelihood: Likelihood[DenseVector[Double], DenseVector[Double], DenseMatrix[Double], (DenseVector[Double], DenseVector[Double])]): Double

    

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    Calculate the marginal log likelihood of the training data for a pre-initialized kernel and noise matrices.

    trainingData

    The function values assimilated as a DenseVector

    kernelMatrix

    The kernel matrix of the training features

    f

    The estimation of mean(f); from the Laplace approximation

13. final def ne(arg0: AnyRef): Boolean

    

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14. final def notify(): Unit

    

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18. final def wait(): Unit

    

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19. final def wait(arg0: Long, arg1: Int): Unit

    

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