Bayesian statistics and modelling.

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Date
2021Author
van de Schoot, Rens
Depaoli, Sarah
King, Ruth
Kramer, Bianca
Märtens, Kaspar
Tadesse, Mahlet G.
Vannucci, Marina
Gelman, Andrew
Veen, Duco
Willemsen, Joukje
Yau, Christopher
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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 .....
Resource URL
https://www.nature.com/articles/s43586-020-00001-2Journal
Nature Reviews Methods PrimersVolume
1Issue
Article 1Page Range
26pp.Document Language
enEssential Ocean Variables (EOV)
N/ADOI Original
https://doi.org/10.1038/ s43586-020-00001-2Citation
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/s43586-020-00001-2Collections
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