An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model
# S4 method for GaussianPrediction show(object) # S4 method for GaussianPrediction component_names(object) # S4 method for GaussianPrediction num_components(object) # S4 method for GaussianPrediction num_paramsets(object) # S4 method for GaussianPrediction num_evalpoints(object)
GaussianPrediction object for which to apply a class method.
show: Print a summary about the object.
component_names: Get names of components.
num_components: Get number of components.
num_paramsets: Get number of parameter combinations
(different parameter vectors) using which predictions were computed.
num_evalpoints: Get number of points where
predictions were computed.
component standard deviations
signal mean (on normalized scale)
signal standard deviation (on normalized scale)
predictive mean (on original data scale)
predictive standard deviation (on original data scale)
a data frame of points (covariate values) where the function posteriors or predictive distributions have been evaluated