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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
Descripción
Sumario: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.