dc.contributor.author | van de Schoot, Rens | |
dc.contributor.author | Depaoli, Sarah | |
dc.contributor.author | King, Ruth | |
dc.contributor.author | Kramer, Bianca | |
dc.contributor.author | Märtens, Kaspar | |
dc.contributor.author | Tadesse, Mahlet G. | |
dc.contributor.author | Vannucci, Marina | |
dc.contributor.author | Gelman, Andrew | |
dc.contributor.author | Veen, Duco | |
dc.contributor.author | Willemsen, Joukje | |
dc.contributor.author | Yau, Christopher | |
dc.date.accessioned | 2022-03-31T13:32:58Z | |
dc.date.available | 2022-03-31T13:32:58Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | van de Schoot, R., Depaoli, S., King, R., et al (2021) Bayesian statistics and modelling. Nature Reviews Methods Primers 1:1, 26pp. DOI: https://doi.org/10.1038/s43586-020-00001-2 | en_US |
dc.identifier.uri | https://repository.oceanbestpractices.org/handle/11329/1902 | |
dc.description.abstract | Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where
available knowledge about parameters in a statistical model is updated with the information in
observed data. The background knowledge is expressed as a prior distribution and combined
with observational data in the form of a likelihood function to determine the posterior distribution.
The posterior can also be used for making predictions about future events. This Primer describes the
stages involved in Bayesian analysis, from specifying the prior and data models to deriving
inference, model checking and refinement. We discuss the importance of prior and posterior
predictive checking, selecting a proper technique for sampling from a posterior distribution,
variational inference and variable selection. Examples of successful applications of Bayesian
analysis across various research fields are provided, including in social sciences, ecology, genetics,
medicine and more. We propose strategies for reproducibility and reporting standards, outlining
an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist.
Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the
next decade. | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Bayesian statistics and modelling. | en_US |
dc.type | Journal Contribution | en_US |
dc.description.refereed | Refereed | en_US |
dc.format.pagerange | 26pp. | en_US |
dc.identifier.doi | https://doi.org/10.1038/ s43586-020-00001-2 | |
dc.subject.parameterDiscipline | Cross-discipline | en_US |
dc.subject.dmProcesses | Data analysis | en_US |
dc.bibliographicCitation.title | Nature Reviews Methods Primers | en_US |
dc.bibliographicCitation.volume | 1 | en_US |
dc.bibliographicCitation.issue | Article 1 | en_US |
dc.description.eov | N/A | en_US |
dc.description.adoption | Validated (tested by third parties) | en_US |
dc.description.methodologyType | Method | en_US |
dc.description.methodologyType | Reports with methodological relevance | en_US |
obps.contact.contactname | Rens van de Schoot | |
obps.contact.contactemail | a.g.j.vandeschoot@uu.nl | |
obps.resourceurl.publisher | https://www.nature.com/articles/s43586-020-00001-2 | |