bcgpmodel-class: An S4 class to represent a BCGP model
In cbdavis33/bcgp: Bayesian Composite Gaussian Processes

Description Slots See Also Examples

This class contains the data, information about the distributions for the parameters, the values of the hyperparameters, along with some other information about the type of model desired to be fit (composite/non-composite, stationary/non-stationary, deterministic/noisy).

data: A list that contains the training data. One element of the list contains the raw data, and one element contains the scaled data in which the independent variables are scaled to [0, 1]^d, and the response variable is scaled to have mean 0 and variance 1.
composite: A logical indicating whether the model is composite or not.
stationary: A logical indicating whether the model is stationary or not.
noise: A logical indicating whether the data is noisy or deterministic (as from a computer model).
priors: A list with an element for each parameter in the BCGP model specified by composite and stationary. Each element contains the values of the hyperparameters for each parameter.
distributions: A list with an element for each parameter in the BCGP model specified by composite and stationary. Each element contains a character string identifying the prior distribution for each parameter.

bcgpmodel

1
2
3

simData <- bcgpsims(composite = TRUE, stationary = FALSE, noise = FALSE)
bcgpmodel(x = simData@training$x, y = simData@training$y
          composite = TRUE, stationary = FALSE, noise = TRUE)