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This function implements additional training iterations for a DGP emulator.


  N = 500,
  cores = 1,
  ess_burn = 10,
  verb = TRUE,
  burnin = NULL,
  B = NULL



an instance of the dgp class.


additional number of iterations for the DGP emulator training. Defaults to 500.


the number of cores/workers to be used to optimize GP components (in the same layer) at each M-step of the training. If set to NULL, the number of cores is set to (max physical cores available - 1). Only use multiple cores when there is a large number of GP components in different layers and optimization of GP components is computationally expensive. Defaults to 1.


number of burnin steps for the ESS-within-Gibbs at each I-step of the training. Defaults to 10.


a bool indicating if the progress bar will be printed during the training:

  1. FALSE: the training progress bar will not be displayed.

  2. TRUE: the training progress bar will be displayed.

Defaults to TRUE.


the number of training iterations to be discarded for point estimates calculation. Must be smaller than the overall training iterations so-far implemented. If this is not specified, only the last 25% of iterations are used. This overrides the value of burnin set in dgp(). Defaults to NULL.


the number of imputations to produce the predictions. Increase the value to account for more imputation uncertainties. This overrides the value of B set in dgp() if B is not NULL. Defaults to NULL.


An updated object.


See further examples and tutorials at


  • One can also use this function to fit an untrained DGP emulator constructed by dgp() with training = FALSE.

  • The following slots:

    • loo and oos created by validate(); and

    • results created by predict() in object will be removed and not contained in the returned object.


if (FALSE) {

# See dgp() for an example.