Deep Gaussian process models for integrating multifidelity experiments with nonstationary relationships
提出一种基于深度高斯过程的模型，通过多层高斯过程对潜在变量进行连续扭曲来捕捉非平稳性，用于整合多保真度实验数据，并使用双重随机变分推断算法进行推理。
The problem of integrating multifidelity data has been studied extensively, due to integrated analyses being able to provide better results than separately analyzing various data types. One popular approach is to use linear autoregressive models with location- and scale-adjustment parameters. Such parameters are typically modeled using stationary Gaussian processes. However, the stationarity assumption may not be appropriate in real-world applications. To introduce nonstationarity for enhanced flexibility, we propose a novel integration model based on deep Gaussian processes that can capture nonstationarity via successive warping of latent variables through multiple layers of Gaussian processes. For inference of the proposed model, we use a doubly stochastic variational inference algorithm. We validate the proposed model using simulated and real-data examples.