Class/Object

scalismo.statisticalmodel

GaussianProcess

Related Docs: object GaussianProcess | package statisticalmodel

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class GaussianProcess[D <: Dim, DO <: Dim] extends AnyRef

A gaussian process from a D dimensional input space, whose input values are points, to a DO dimensional output space. The output space is a Euclidean vector space of dimensionality DO.

D

The dimensionality of the input space

DO

The dimensionality of the output space

Linear Supertypes
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Known Subclasses
LowRankGaussianProcess
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Instance Constructors

  1. new GaussianProcess(mean: VectorField[D, DO], cov: MatrixValuedPDKernel[D, DO])(implicit arg0: NDSpace[D], arg1: NDSpace[DO])

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    mean

    The mean function

    cov

    The covariance function. Needs to be positive definite

    Attributes
    protected

Value Members

  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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  6. val cov: MatrixValuedPDKernel[D, DO]

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    The covariance function.

    The covariance function. Needs to be positive definite

  7. val dimOps: NDSpace[DO]

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  8. def domain: Domain[D]

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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def marginal(pt: Point[D]): NDimensionalNormalDistribution[DO]

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    Compute the marginal distribution at a single point.

  16. def marginal(domain: DiscreteDomain[D]): DiscreteGaussianProcess[D, DO]

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    Compute the marginal distribution for the given points.

    Compute the marginal distribution for the given points. The result is again a Gaussian process, whose domain is defined by the given points.

  17. val mean: VectorField[D, DO]

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    The mean function

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def posterior(trainingData: IndexedSeq[(Point[D], Vector[DO], NDimensionalNormalDistribution[DO])]): GaussianProcess[D, DO]

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    The posterior distribution of the gaussian process, with respect to the given trainingData.

    The posterior distribution of the gaussian process, with respect to the given trainingData. It is computed using Gaussian process regression.

  22. def posterior(trainingData: IndexedSeq[(Point[D], Vector[DO])], sigma2: Double): GaussianProcess[D, DO]

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    The posterior distribution of the gaussian process, with respect to the given trainingData.

    The posterior distribution of the gaussian process, with respect to the given trainingData. It is computed using Gaussian process regression. We assume that the trainingData is subject to isotropic Gaussian noise with variance sigma2.

  23. def sampleAtPoints(domain: DiscreteDomain[D]): DiscreteVectorField[D, DO]

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    Sample values of the Gaussian process evaluated at the given points.

  24. final def synchronized[T0](arg0: ⇒ T0): T0

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  25. def toString(): String

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  26. final def wait(): Unit

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

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  28. final def wait(arg0: Long): Unit

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