Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis

• URL: http://arxiv.org/abs/2405.10839v2
• Date: Tue, 20 Aug 2024 05:38:01 GMT
• Title: Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis
• Authors: Pablo Giuliani, Kyle Godbey, Vojtech Kejzlar, Witold Nazarewicz,
• Abstract summary: In many cases, the models used in the mixing process are similar. The existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. We show that by adding modelization to the proposed Bayesian Model Combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 work 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.

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