Abstract

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.

摘要

集成多保真数据的问题已得到广泛研究，因为集成分析能够提供比单独分析各种数据类型更好的结果。一种流行的方法是使用具有位置和比例调整参数的线性自回归模型。这些参数通常使用平稳高斯过程建模。然而，平稳性假设可能不适用于实际应用。为了引入非平稳性以提高灵活性，我们提出了一种基于深度高斯过程的新型集成模型，该模型可以通过多层高斯过程对潜在变量进行连续扭曲来捕获非平稳性。对于所提出的模型的推理，我们使用双重随机变分推理算法。