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object GaussianProcess

Factory methods for creating Gaussian processes

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  4. def apply[D, Value](cov: MatrixValuedPDKernel[D])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): GaussianProcess[D, Value]

    Creates a new zero-mean Gaussian process with the given covariance function.

  5. def apply[D, Value](mean: Field[D, Value], cov: MatrixValuedPDKernel[D])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): GaussianProcess[D, Value]

    Creates a new Gaussian process with given mean and covariance, which is defined on the given domain.

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  14. def marginalLikelihood[D, Value](gp: GaussianProcess[D, Value], trainingData: IndexedSeq[(Point[D], Value, MultivariateNormalDistribution)])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): Double

    * Computes the marginal likelihood of the observed data, according to the given GP.

    * Computes the marginal likelihood of the observed data, according to the given GP.

    This can for example be used in a model selection setting, where the GP with the maximum marginal likelihood of the observed data would be selected.

    gp

    The gaussian process

    trainingData

    Point/value pairs where that the sample should approximate, together with an error model (the uncertainty) at each point.

    To do

    The current implementation can be optimized as it inverts the data covariance matrix (that can be heavy for more than a few points). Instead an implementation with a Cholesky decomposition would be more efficient.

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  18. def regression[D, Value](gp: GaussianProcess[D, Value], trainingData: IndexedSeq[(Point[D], Value, MultivariateNormalDistribution)])(implicit arg0: NDSpace[D], vectorizer: Vectorizer[Value]): GaussianProcess[D, Value]

    * Performs a Gaussian process regression, where we assume that each training point (vector) is subject to zero-mean noise with given variance.

    * Performs a Gaussian process regression, where we assume that each training point (vector) is subject to zero-mean noise with given variance.

    gp

    The gaussian process

    trainingData

    Point/value pairs where that the sample should approximate, together with an error model (the uncertainty) at each point.

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