Introduction to Bayesian inference for psychology


We introduce the fundamental tenets of Bayesian inference, which derive from two basic laws of probability theory. We cover the interpretation of probabilities, discrete and continuous versions of Bayes' rule, parameter estimation, and model comparison. Using seven worked examples, we illustrate these principles and set up some of the technical background for the rest of this special issue of Psychonomic Bulletin & Review. Supplemental material is available via


Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25, 5–34.


    title   = {{I}ntroduction to {B}ayesian inference for psychology},
    author  = {Etz, Alexander and Vandekerckhove, Joachim},
    year    = {2018},
    journal = {Psychonomic Bulletin \& Review},
    volume  = {25},
    pages   = {5--34}