DiscreteLowRankGaussianProcess
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object DiscreteLowRankGaussianProcess
| package statisticalmodel
Discrete version of Vector[DO], Double)])
Discrete version of DiscreteLowRankGaussianProcess.instance
Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess, using nearest neigbor interpolation (for both mean and covariance function)
Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess, using nearest neigbor interpolation (for both mean and covariance function)
Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess.
Interpolates discrete Gaussian process to have a new, continuous representation as a DiscreteLowRankGaussianProcess. This is achieved by using a Nystrom method for computing the kl basis. The mean function is currently interpolated using a nearest neighbor approach.
determines how many points of the domain are used to estimate the full kl basis.
Returns the variance and associated basis function that defines the process.
Returns the variance and associated basis function that defines the process. The basis is the (discretized) Karhunen Loeve basis (e.g. it is obtained from a Mercer's decomposition of the covariance function
Returns the log of the probability density of the instance
Returns the log of the probability density of the instance
If you are interested in ordinal comparisons of PDFs, use this as it is numerically more stable
Returns the log of the probability density of the instance produced by the x coefficients.
Returns the log of the probability density of the instance produced by the x coefficients.
If you are interested in ordinal comparisons of PDFs, use this as it is numerically more stable
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.
The marginal distribution at a given (single) point, specified by the pointId.
The marginal distribution at a given (single) point, specified by the pointId.
Returns the probability density of the given instance
Returns the probability density of the given instance
Returns the probability density of the instance produced by the x coefficients
Discrete version of Vector[DO], Double)]).
Discrete version of Vector[DO], Double)]). In contrast to this method, the points for the training data are defined by the pointId. The returned posterior process is defined at the same points.
Discrete version of Vector[DO])], sigma2: Double.
Discrete version of Vector[DO])], sigma2: Double. In contrast to this method, the points for the training data are defined by the pointId. The returned posterior process is defined at the same points.
Discrete version of Vector[DO])], Double)
Discrete version of Vector[DO])], Double)
Discrete version of DiscreteLowRankGaussianProcess.sample
Discrete version of DiscreteLowRankGaussianProcess.sample
Represents a low-rank gaussian process, that is only defined at a finite, discrete set of points. It supports the same operations as the LowRankGaussianProcess class, but always returns instead a discrete representation. Furthermore, most operations are much more efficient, as they are implemented using fast matrix/vector operations.
Where the modeled functions in a LowRankGaussianProcess are of type Point[D]=>Vector[D], this discretized version is of type VectorPointData.
It is possible to convert a DiscreteLowRankGaussianProcess to a LowRankGaussianProcess by calling the interpolation method.
DiscreteLowRankGaussianProcess
scalismo.common.DiscreteVectorField