learnParameters performs maximum likelihood parameter estimation in the PSGP framework.
a list object of intamap type. Most arguments necessary for
interpolation are passed through this object.
The Projected Spatial Gaussian Process (PSGP) framework provides an approximation to the full Gaussian process in which the observations are projected sequentially onto an optimal subset of 'active' observations. Spatial interpolation is done using a mixture of covariance kernels (exponential and Matern 5/2).
learnParameters is an internal function for estimating the
parameters of the covariance function given the data, using a maximum likelihood
approach. A valid intamap
object must be passed in.
PSGP is able to also take the measurement characteristics (i.e. errors) into
account using possibly many error models. For each error model, assumed Gaussian, the
error variance can be specified. The vector
object$observations$oevar contains all variances for the error models (one
value per error model).
Which error model is used for a given observation is determined by the
object$observations$oeid vector of indices, which specifies the index of the
model to be used for each observation.
It is advised to use the intamap wrapper
estimateParameters rather than calling this method directly.
Ben Ingram, Remi Barillec
Csato and Opper, 2002; Ingram et al., 2008
# see example in estimateParameters
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