Editorial: Moving Beyond Non-informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research

Christoph Koenig; Sarah Depaoli; Haiyan Liu; Rens van de Schoot

doi:10.3389/fpsyg.2021.809719

Editorial: Moving Beyond Non-informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research

Front Psychol. 2021 Dec 9:12:809719. doi: 10.3389/fpsyg.2021.809719. eCollection 2021.

Authors

Christoph Koenig¹, Sarah Depaoli², Haiyan Liu², Rens van de Schoot³

Affiliations

¹ Department of Educational Psychology, Goethe University Frankfurt am Main, Frankfurt, Germany.
² Department of Psychological Sciences, University of California, Merced, Merced, CA, United States.
³ Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands.

No abstract available

Keywords: Bayesian modeling and inference; Bayesian statistics; cumulative science; informative prior distributions; prior distributions; psychological research.

Publication types

Editorial