The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models

We introduce a zero-censored Gaussian process as a systematic, model-based approach to building Gaussian process emulators for range-constrained simulator output. This approach avoids many pitfalls associated with modeling range-constrained data with Gaussian processes. Further, it is flexible enough to be used in conjunction with statistical emulator advancements such as emulators that model high-dimensional vector-valued simulator output. The zero-censored Gaussian process is then applied to two examples of geophysical flow inundation which have the constraint of nonnegativity.

Duke Scholars

Author Robert L. Wolpert Statistical Science