2024/08/02 by Barnett, Steven D., Lauren J. Beesley, Annie S. Booth +6 · 2 citations
Computer Science · Engineering · #Computation (stat.CO) #FOS: Computer and information sciences #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2408.01540
openalex publication_date 2024/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Gaussian processes (GPs) are canonical as surrogates for computer experiments because they enjoy a degree of analytic tractability. But that breaks when the response surface is constrained, say to be monotonic. Here, we provide a mono-GP construction for a single input that is highly efficient even though the calculations are non-analytic. Key ingredients include transformation of a reference process and elliptical slice sampling. We then show how mono-GP may be deployed effectively in two ways. One is additive, extending monotonicity to more inputs; the other is as a prior on injective latent warping variables in a deep Gaussian process for (non-monotonic, multi-input) non-stationary surrogate modeling. We provide illustrative and benchmarking examples throughout, showing that our methods yield improved performance over the state-of-the-art on examples from those two classes of problems.