  Computer models, also known as simulators, can be computationally expensive
to run, and for this reason statistical surrogates, known as emulators, are
often used. Any statistical model, including an emulator, should be validated
before being used, otherwise resulting decisions can be misguided. We discuss
how current methods for validating Gaussian process emulators of deterministic
models are insufficient for emulators of stochastic computer models and develop
a framework for diagnosing problems in stochastic emulators. These diagnostics
are based on independently validating the mean and variance predictions using
out-of-sample, replicated, simulator runs. We then also use a building
performance simulator as a case study example.
