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BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis

Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such mo...

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Detalles Bibliográficos
Autores principales: Song, Jaejoon, Morita, Satoshi, Kuo, Ying-Wei, Lee, J. Jack
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299797/
https://www.ncbi.nlm.nih.gov/pubmed/37377886
http://dx.doi.org/10.1016/j.softx.2023.101358
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author Song, Jaejoon
Morita, Satoshi
Kuo, Ying-Wei
Lee, J. Jack
author_facet Song, Jaejoon
Morita, Satoshi
Kuo, Ying-Wei
Lee, J. Jack
author_sort Song, Jaejoon
collection PubMed
description Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such modeling framework has been lacking. In this article, we present BayesESS, a comprehensive, free, and open source R package for quantifying the impact of parametric priors in Bayesian analysis. We also introduce an accompanying web-based application for estimating and visualizing Bayesian effective sample size for purposes of conducting or planning Bayesian analyses.
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spelling pubmed-102997972023-06-27 BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis Song, Jaejoon Morita, Satoshi Kuo, Ying-Wei Lee, J. Jack SoftwareX Article Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such modeling framework has been lacking. In this article, we present BayesESS, a comprehensive, free, and open source R package for quantifying the impact of parametric priors in Bayesian analysis. We also introduce an accompanying web-based application for estimating and visualizing Bayesian effective sample size for purposes of conducting or planning Bayesian analyses. 2023-05 2023-03-26 /pmc/articles/PMC10299797/ /pubmed/37377886 http://dx.doi.org/10.1016/j.softx.2023.101358 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ).
spellingShingle Article
Song, Jaejoon
Morita, Satoshi
Kuo, Ying-Wei
Lee, J. Jack
BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title_full BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title_fullStr BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title_full_unstemmed BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title_short BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis
title_sort bayesess: a tool for quantifying the impact of parametric priors in bayesian analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10299797/
https://www.ncbi.nlm.nih.gov/pubmed/37377886
http://dx.doi.org/10.1016/j.softx.2023.101358
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