Eliciting Gaussian process priors for complex computer codes

Summary. We consider the problem of eliciting expert knowledge about the output of a deterministic computer code, where the output is a function of a vector of input variables. A Gaussian process prior is assumed for the unknown function, and expert judgments about the output at various inputs are used to find suitable hyperparameters of the Gaussian process prior distribution. An example is presented involving the movement of radionuclides in the food chain.