Model orthogonalization and Bayesian forecast mixing via principal component analysis

P. Giuliani, K. Godbey, V. Kejzlar, W. Nazarewicz
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引用次数: 0

Abstract

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this paper we describe a method based on the principal component analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian model combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.

Abstract Image

通过主成分分析实现模型正交化和贝叶斯预测混合
人们可以利用贝叶斯统计机器学习框架,将不完善的复杂计算模型的预测结合起来,从而提高未知领域的可预测性。然而,在许多情况下,混合过程中使用的模型是相似的。在多模型过程中,除了会污染模型空间外,这种相似甚至冗余模型的存在还会导致对结果的误读和预测性能的下降。本文介绍了一种基于主成分分析的消除模型冗余的方法。我们的研究表明,通过在所提出的贝叶斯模型组合框架中加入模型正交化,可以获得更好的预测精度和出色的不确定性量化性能。
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