Data Analysis, Statistics and Probability

New submissions

• New submissions
• Replacements

New submissions for Mon, 20 May 24

[1]  arXiv:2405.10839 [pdf, other]

Title: Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis

Authors: Pablo Giuliani, Kyle Godbey, Vojtech Kejzlar, Witold Nazarewicz

Comments: 12 pages, 4 figures

Subjects: Nuclear Theory (nucl-th); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)

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.

Replacements for Mon, 20 May 24

[2]  arXiv:2207.12855 (replaced) [pdf, other]

Title: Efficient Learning of Accurate Surrogates for Simulations of Complex Systems

Authors: A. Diaw, M. McKerns, I. Sagert, L. G. Stanton, M. S. Murillo

Comments: 13 pages, 6 figures, submitted to Nature Machine Intelligence

Subjects: Machine Learning (cs.LG); Nuclear Theory (nucl-th); Computational Physics (physics.comp-ph); Data Analysis, Statistics and Probability (physics.data-an); Plasma Physics (physics.plasm-ph)

[3]  arXiv:2405.09817 (replaced) [pdf, other]

Title: Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data

Authors: Maxim Ziatdinov

Comments: Fixed PGM in Figure 2 and update caption

Subjects: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)

• New submissions
• Replacements