Bayesian statistics and modelling.
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van de Schoot, Rens
Tadesse, Mahlet G.
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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 .....
JournalNature Reviews Methods Primers
Essential Ocean Variables (EOV)N/A
DOI Originalhttps://doi.org/10.1038/ s43586-020-00001-2
Citationvan 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
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