# Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis

arXiv: https://arxiv.org/abs/2405.10839 · HTML: https://mlsanity.com/paper/2405.10839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 8 of 8)

- [Using stacking to average Bayesian predictive distributions](https://mlsanity.com/paper/1704.02030/) (2017)
- [Bayesian model averaging: A systematic review and conceptual classification](https://mlsanity.com/paper/1509.08864/) (2015)
- [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://mlsanity.com/paper/2101.08954/) (2021)
- [Bayesian approach to model-based extrapolation of nuclear observables](https://mlsanity.com/paper/1806.00552/) (2018)
- [Quantified limits of the nuclear landscape](https://mlsanity.com/paper/2001.05924/) (2020)
- [Neutron drip line in the Ca region from Bayesian model averaging](https://mlsanity.com/paper/1901.07632/) (2019)
- [Statistical aspects of nuclear mass models](https://mlsanity.com/paper/2002.04151/) (2020)
- [Local Bayesian Dirichlet mixing of imperfect models](https://mlsanity.com/paper/2311.01596/) (2023)

## Built on by (most cited first, 1 of 1)

- [Global Framework for Emulation of Nuclear Calculations](https://mlsanity.com/paper/2502.20363/) (2025)

## Abstract (arXiv)

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.