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class DiscreteGaussianProcess[D, DDomain[D] <: DiscreteDomain[D], Value] extends AnyRef

A representation of a gaussian process, which is only defined on a discrete domain. While this is technically similar to a MultivariateNormalDistribution, we highlight with this class that we represent (discrete) functions, defined on the given domain.

Self Type
DiscreteGaussianProcess[D, DDomain, Value]
Linear Supertypes
AnyRef, Any
Known Subclasses
DiscreteLowRankGaussianProcess
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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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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. val cov: DiscreteMatrixValuedPDKernel[D]
  7. val domain: DDomain[D]
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: AnyRef): Boolean
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  10. def finalize(): Unit
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  11. final def getClass(): Class[_ <: AnyRef]
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    @native()
  12. def hashCode(): Int
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    @native()
  13. def interpolate(interpolator: FieldInterpolator[D, DDomain, Value]): GaussianProcess[D, Value]
  14. final def isInstanceOf[T0]: Boolean
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  15. def logpdf(instance: DiscreteField[D, DDomain, Value]): Double

    Returns the log of the probability density of the given instance

    Returns the log of the probability density of the given instance

    If you are interested in ordinal comparisons of PDFs, use this as it is numerically more stable

  16. def marginal(pointIds: Seq[PointId])(implicit domainCreator: Create[D]): DiscreteGaussianProcess[D, UnstructuredPointsDomain, Value]

    The marginal distribution for the points specified by the given point ids.

    The marginal distribution for the points specified by the given point ids. Note that this is again a DiscreteGaussianProcess.

  17. def marginal(pointId: PointId): MultivariateNormalDistribution

    The marginal distribution at a given (single) point, specified by the pointId.

  18. val mean: DiscreteField[D, DDomain, Value]
  19. final def ne(arg0: AnyRef): Boolean
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  20. final def notify(): Unit
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    @native()
  21. final def notifyAll(): Unit
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  22. val outputDim: Int
  23. def pdf(instance: DiscreteField[D, DDomain, Value]): Double

    Returns the probability density of the given instance

  24. def project(s: DiscreteField[D, DDomain, Value]): DiscreteField[D, DDomain, Value]

    Discrete version of Vector[DO])], Double)

  25. def sample()(implicit rand: Random): DiscreteField[D, DDomain, Value]
  26. final def synchronized[T0](arg0: => T0): T0
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  27. def toString(): String
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  28. implicit val vectorizer: Vectorizer[Value]
  29. final def wait(): Unit
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  30. final def wait(arg0: Long, arg1: Int): Unit
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  31. final def wait(arg0: Long): Unit
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Deprecated Value Members

  1. def interpolateNearestNeighbor: GaussianProcess[D, Value]

    Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess, using nearest neighbor interpolation (for both mean and covariance function)

    Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess, using nearest neighbor interpolation (for both mean and covariance function)

    Annotations
    @deprecated
    Deprecated

    (Since version 0.16) please use the interpolate method with a NearestNeighborInterpolator instead

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