Paper: Bayesian statistics and modelling
By Rens van de Schoot et al, Nature Reviews
Bayesian statistics and modelling is an open access paper published by Nature Reviews as part of its first volume of Methods Primers.
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 paper describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. The authors 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. The authors also 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, the paper outlines the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade.
Read the paper.