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Prior selection method using likelihood confidence region and Dirichlet process Gaussian mixture model for Bayesian inference of building energy models

机译：使用似然置信区和Dirichlet工艺Gaussian推理的Dirichlet工艺Gaussian混合模型的先前选择方法

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摘要

It is widely acknowledged that Bayesian inference is only beneficial when prior information is properly defined. However, there is no clear rule for prior selection, and it is apparently a matter of subjective selection by the domain expert(s). In other words, because the posterior inference results can vary depending on how the prior is set, a proper definition of the prior is important in terms of objectivity and accuracy for Bayesian inference of building energy models. Hence, the authors suggest a new prior selection method using Dirichlet process Gaussian mixture model (DPGMM) and the likelihood confidence region (hereafter referred to as likelihood CR). The DPGMM is a Bayesian nonparametric clustering technique that optimizes both the cluster shape and the number of clusters. Using the DPGMM, the finite probability distributions that make up the likelihood CR can be estimated, where the distribution with the highest maximum likelihood is applied as the informative prior. In this study, a reference office building of the United States Department of Energy was selected, and a surrogate model was generated using an artificial neural network. Based on a comparison between the authors' suggestion and traditional informative (and/or non-informative) priors by domain experts, the proposed method requires only minimum information about the parameters (min and max) and performs better than the traditional approach (c) 2020 Elsevier B.V. All rights reserved.
机译：广泛认识到，贝叶斯推理仅在正确定义之前的信息时才有益。但是，目前选择没有明确的规则，显然是域专家的主观选择问题。换句话说，因为后部推断结果可以根据先前设置的方式而变化，所以在建筑能量模型的贝叶斯推理的客观性和准确性方面，前提的正确定义是重要的。因此，作者建议使用Dirichlet工艺高斯混合模型（DPGMM）的新的先前选择方法和似然置信区（以下称为似然CR）。 DPGMM是贝叶斯非参数聚类技术，可优化群集形状和群集数。使用DPGMM，可以估计构成可能性CR的有限概率分布，其中最大可能性最高可能性的分布作为信息性。在本研究中，选择了美国能源部的参考办公楼，使用人工神经网络产生了代理模型。基于作者建议和传统信息（和/或非信息性）前瞻的域专家的比较，所提出的方法只需要有关参数的最低信息（最小和最大值），并且比传统方法更好地执行（C） 2020 Elsevier BV保留所有权利。

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