Cargando…
Local Bayesian Dirichlet mixing of imperfect models
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Ba...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638441/ https://www.ncbi.nlm.nih.gov/pubmed/37949993 http://dx.doi.org/10.1038/s41598-023-46568-0 |
_version_ | 1785133599065899008 |
---|---|
author | Kejzlar, Vojtech Neufcourt, Léo Nazarewicz, Witold |
author_facet | Kejzlar, Vojtech Neufcourt, Léo Nazarewicz, Witold |
author_sort | Kejzlar, Vojtech |
collection | PubMed |
description | To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations. |
format | Online Article Text |
id | pubmed-10638441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106384412023-11-11 Local Bayesian Dirichlet mixing of imperfect models Kejzlar, Vojtech Neufcourt, Léo Nazarewicz, Witold Sci Rep Article To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations. Nature Publishing Group UK 2023-11-10 /pmc/articles/PMC10638441/ /pubmed/37949993 http://dx.doi.org/10.1038/s41598-023-46568-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kejzlar, Vojtech Neufcourt, Léo Nazarewicz, Witold Local Bayesian Dirichlet mixing of imperfect models |
title | Local Bayesian Dirichlet mixing of imperfect models |
title_full | Local Bayesian Dirichlet mixing of imperfect models |
title_fullStr | Local Bayesian Dirichlet mixing of imperfect models |
title_full_unstemmed | Local Bayesian Dirichlet mixing of imperfect models |
title_short | Local Bayesian Dirichlet mixing of imperfect models |
title_sort | local bayesian dirichlet mixing of imperfect models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638441/ https://www.ncbi.nlm.nih.gov/pubmed/37949993 http://dx.doi.org/10.1038/s41598-023-46568-0 |
work_keys_str_mv | AT kejzlarvojtech localbayesiandirichletmixingofimperfectmodels AT neufcourtleo localbayesiandirichletmixingofimperfectmodels AT nazarewiczwitold localbayesiandirichletmixingofimperfectmodels |