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class GaussianProcess[D, Value] 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

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Known Subclasses
LowRankGaussianProcess
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Instance Constructors

  1. new GaussianProcess(mean: Field[D, Value], cov: MatrixValuedPDKernel[D])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value])

    mean

    The mean function

    cov

    The covariance function. Needs to be positive definite

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]
  7. def discretize[DDomain[DD] <: DiscreteDomain[DD]](domain: DDomain[D]): DiscreteGaussianProcess[D, DDomain, Value]

    Discretizes the Gaussian Process at the given domain points.

    Discretizes the Gaussian Process at the given domain points. The

  8. def domain: Domain[D]
  9. final def eq(arg0: AnyRef): Boolean
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  10. def equals(arg0: AnyRef): Boolean
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  11. def finalize(): Unit
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  12. final def getClass(): Class[_ <: AnyRef]
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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]): MultivariateNormalDistribution

    Compute the marginal distribution at a single point.

  16. def marginal(points: IndexedSeq[Point[D]])(implicit domainCreator: Create[D]): DiscreteGaussianProcess[D, UnstructuredPointsDomain, Value]

    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 an unstructured points domain

  17. val mean: Field[D, Value]
  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 outputDim: Int
  22. def posterior(trainingData: IndexedSeq[(Point[D], Value, MultivariateNormalDistribution)]): GaussianProcess[D, Value]

    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.

  23. def posterior(trainingData: IndexedSeq[(Point[D], Value)], sigma2: Double): GaussianProcess[D, Value]

    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.

  24. def sampleAtPoints[DDomain[DD] <: DiscreteDomain[DD]](domain: DDomain[D])(implicit rand: Random): DiscreteField[D, DDomain, Value]

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

  25. final def synchronized[T0](arg0: => T0): T0
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  26. def toString(): String
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  27. implicit val vectorizer: Vectorizer[Value]
  28. final def wait(): Unit
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  29. final def wait(arg0: Long, arg1: Int): Unit
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  30. final def wait(arg0: Long): Unit
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