Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points.
In this analysis we focus on stochastic kriging metamodels.
We show that if this type of metamodel is used and we assume that its metaparameters are fixed, then updating such a metamodel with new observations is equivalent to a Bayesian forecast combination under the known variance assumption.
This is useful when the two articles are not on the same page - the articles will be remembered between pages.
Cite ULike organises scholarly (or academic) papers or literature and provides bibliographic (which means it makes bibliographies) for universities and higher education establishments. People studying for Ph Ds or in postdoctoral (postdoc) positions.
Here, we propose a novel Gaussian-process based approach for solving games in this context.
We follow a classical Bayesian optimization framework, with sequential sampling decisions based on acquisition functions.
In this work we propose a methodology that enables both approaches to be combined.
When a prediction for a new input is required the procedure is to augment the metamodel forecast with additional simulation outputs for a given input.
However, the Kriging variance and covariance formulae given without proof in Emery (2009) for the batch-sequential case are not correct.