SPGP-class: Sparse Gaussian Process
In italo-goncalves/geomod3D: Implicit 3D Geological Modeling and Geostatistics

Description Usage Slots References See Also

Implementation of the Sparse Gaussian Process model for 3D spatial interpolation. Extends the GP class.

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SPGP(data, model, value, mean = NULL, trend = NULL, pseudo_inputs = data,
  force.interp = numeric(), reg.v = 1e-09, tangents = NULL,
  reg.t = 1e-12, pseudo_tangents = tangents, variational = T)

data: A spatial3DDataFrame object containing the necessary data.
tangents: A directions3DDataFrame object containing structural geology data. Most likely generated with the GetLineDirections() or GetPlaneDirections() method.
model: The covariance model. A covarianceModel3D object.
mean: The global mean. Irrelevant if a trend is used.
trend: The model's trend component. A formula in character format.
beta: The trend coefficients.
likelihood: The model's log-likelihood given the data.
pre_comp: A list containing pre-computed values to speed up predictions.
pseudo_inputs: A spatial3DDataFrame object containing the coordinates of the pseudo-inputs.
pseudo_tangents: A directions3DDataFrame object containing the coordinates of the pseudo-inputs for directional data.
variational: A logical indicating if the model uses the variational approach.

Snelson, E., Ghahramani, Z., 2006. Sparse Gaussian Processes using Pseudo-inputs. Adv. Neural Inf. Process. Syst. 18 1257<e2><80><93>1264.

Titsias, M., 2009. Variational Learning of Inducing Variables in Sparse Gaussian Processes. Aistats 5, 567<e2><80><93>574.

Bauer, M.S., van der Wilk, M., Rasmussen, C.E., 2016. Understanding Probabilistic Sparse Gaussian Process Approximations. Adv. Neural Inf. Process. Syst. 29.

SPGP-init, GP-class

italo-goncalves/geomod3D documentation built on May 24, 2019, 2:49 p.m.