Bayesian statistics and modelling

Rens van de Schoot, Sarah Depaoli, Andrew Gelman, Ruth King, Bianca Kramer, Kaspar Märtens, Mahlet G. Tadesse, Marina Vannucci, Joukje Willemsen, Christopher Yau

Research output: Contribution to journalArticlepeer-review

Abstract

Bayesian statistics is an approach to data analysis and parameter estimation based on Bayes’ Theorem. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models, to deriving inference, model checking and refinement. Bayesian analysis has been successfully employed across a variety of research fields, including social sciences, ecology, genetics, medicine, and more. We discuss these applications and propose strategies for reproducibility and reporting standards. Finally, we outline the impact of Bayesian analysis in artificial intelligence, a major goal in the next decade.
Original languageEnglish
Number of pages60
JournalNature Reviews Methods Primers
Publication statusAccepted/In press - 21 Oct 2020

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