Bayesian inference for psychology, part III: Parameter estimation in nonstandard models

Dora Matzke; Udo Boehm; Joachim Vandekerckhove

doi:10.3758/s13423-017-1394-5

Bayesian inference for psychology, part III: Parameter estimation in nonstandard models

Psychon Bull Rev. 2018 Feb;25(1):77-101. doi: 10.3758/s13423-017-1394-5.

Authors

Dora Matzke¹, Udo Boehm², Joachim Vandekerckhove³

Affiliations

¹ University of Amsterdam, Amsterdam, Netherlands.
² University of Groningen, Groningen, Netherlands.
³ University of California, Irvine, CA, USA. joachim@uci.edu.

PMID: 29134543
DOI: 10.3758/s13423-017-1394-5

Abstract

We demonstrate the use of three popular Bayesian software packages that enable researchers to estimate parameters in a broad class of models that are commonly used in psychological research. We focus on WinBUGS, JAGS, and Stan, and show how they can be interfaced from R and MATLAB. We illustrate the use of the packages through two fully worked examples; the examples involve a simple univariate linear regression and fitting a multinomial processing tree model to data from a classic false-memory experiment. We conclude with a comparison of the strengths and weaknesses of the packages. Our example code, data, and this text are available via https://osf.io/ucmaz/ .

Keywords: Bayesian estimation; Bayesian inference; JAGS; Stan; WinBUGS.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Bayes Theorem*
Humans
Linear Models
Psychology*
Software*

Abstract

Publication types

MeSH terms

Grants and funding