论文标题
使用藤蔓的变异推断：贝叶斯计算机模型校准的有效方法
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration
论文作者
论文摘要
随着计算机架构的进步，计算模型的使用扩增以解决许多科学应用，例如核物理和气候研究。但是，这种模型的潜力通常会受到阻碍，因为它们在计算上往往是昂贵的，因此对于不确定性定量而言是不合适的。此外，通常不会通过实时观察来校准它们。我们基于变异贝叶斯推理（VBI）开发了一种计算有效算法，用于使用高斯过程的计算机模型进行校准。不幸的是，使用依赖数据将VBI的速度和可扩展性降低会降低校准框架。为了保持VBI的效率，我们使用藤本植物对数据可能性的成对分解，将数据中数据依赖性结构的信息与其边际分布区分开。我们为我们的方法的计算可扩展性提供了理论和经验证据，并描述了有效实施所提出算法的所有必要细节。我们还通过校准核结合能的液滴模型来证明我们的方法为从业人员提供了实践者的机会。
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research. However, the potential of such models is often hindered because they tend to be computationally expensive and consequently ill-fitting for uncertainty quantification. Furthermore, they are usually not calibrated with real-time observations. We develop a computationally efficient algorithm based on variational Bayes inference (VBI) for calibration of computer models with Gaussian processes. Unfortunately, the speed and scalability of VBI diminishes when applied to the calibration framework with dependent data. To preserve the efficiency of VBI, we adopt a pairwise decomposition of the data likelihood using vine copulas that separate the information on dependence structure in data from their marginal distributions. We provide both theoretical and empirical evidence for the computational scalability of our methodology and describe all the necessary details for an efficient implementation of the proposed algorithm. We also demonstrate the opportunities given by our method for practitioners on a real data example through calibration of the Liquid Drop Model of nuclear binding energies.
