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Activity Number: 327 - On Surrogate Modeling of Emerging Issues in Physical and Engineering Simulators
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #320832
Title: A Nonstationary Soft Partitioned Gaussian Process Model via Random Spanning Trees
Author(s): Zhao Tang Luo and Huiyan Sang* and Bani Mallick
Companies: Texas A&M University and Texas A&M University and Texas A&M University
Keywords: Bayesian posterior concentration; Gaussian process; Locally stationary models; Random spanning trees

There has been a long-standing challenge in developing locally stationary Gaussian process (GP) models concerning how to obtain flexible partitions and how to do predictions near boundaries. In this work, we develop a new class of locally stationary stochastic processes, where local partitions are modeled by a soft partition process via predictive random spanning trees for highly flexible spatially contiguous cluster shapes. This valid nonstationary process model allows to knit together local models such that both parameter estimation and prediction can be performed under a unified and coherent framework, and to capture both discontinuities/abrupt changes and smoothness in a spatial random field. We propose a theoretical framework to study the Bayesian posterior concentration concerning the behavior of this Bayesian nonstationary process model. The performance of the proposed model is illustrated with simulation studies and a real data analysis.

Authors who are presenting talks have a * after their name.

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