2023/10/31 by D. Liyanage, Ingles, Kevin, A. C. Semposki +4 · 1 citation
Chemistry · Computer Science · Engineering · #Computation (stat.CO) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Gaussian Processes and Bayesian Inference #Mass Spectrometry Techniques and Applications #Nuclear Theory (nucl-th) #Nuclear reactor physics and engineering #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2310.20549
openalex publication_date 2023/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Uncertainty quantification using Bayesian methods is a growing area of research. Bayesian model mixing (BMM) is a recent development which combines the predictions from multiple models such that each model's best qualities are preserved in the final result. Practical tools and analysis suites that facilitate such methods are therefore needed. Taweret introduces BMM to existing Bayesian uncertainty quantification efforts. Currently Taweret contains three individual Bayesian model mixing techniques, each pertaining to a different type of problem structure; we encourage the future inclusion of user-developed mixing methods. Taweret's first use case is in nuclear physics, but the package has been structured such that it should be adaptable to any research engaged in model comparison or model mixing.