eprintid: 10157434 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/15/74/34 datestamp: 2022-10-18 16:50:50 lastmod: 2022-10-18 16:50:50 status_changed: 2022-10-18 16:50:50 type: article metadata_visibility: show sword_depositor: 699 creators_name: Ming, Deyu creators_name: Williamson, Daniel creators_name: Guillas, Serge title: Deep Gaussian Process Emulation using Stochastic Imputation ispublished: inpress divisions: C06 divisions: F61 divisions: B04 divisions: UCL divisions: C05 divisions: F49 keywords: Elliptical slice sampling, Linked Gaussian processes, Option Greeks, Surrogate model, Stochastic expectation maximization note: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. abstract: Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally using the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A Python package dgpsi implementing the method is also produced and available at https://github.com/mingdeyu/DGP. date: 2022-10-12 date_type: published publisher: Taylor & Francis official_url: https://doi.org/10.1080/00401706.2022.2124311 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1877530 doi: 10.1080/00401706.2022.2124311 lyricists_name: Guillas, Serge lyricists_name: Ming, Deyu lyricists_id: SGUIL73 lyricists_id: MINGX71 actors_name: Ming, Deyu actors_id: MINGX71 actors_role: owner full_text_status: public publication: Technometrics citation: Ming, Deyu; Williamson, Daniel; Guillas, Serge; (2022) Deep Gaussian Process Emulation using Stochastic Imputation. Technometrics 10.1080/00401706.2022.2124311 <https://doi.org/10.1080/00401706.2022.2124311>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10157434/1/Deep%20Gaussian%20Process%20Emulation%20using%20Stochastic%20Imputation.pdf