The JASP guidelines for conducting and reporting a Bayesian analysis

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Abstract

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

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Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.

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Author and article information

Contributors

Johnny van Doorn: JohnnyDoorn@gmail.com

Journal

Journal ID (nlm-ta): Psychon Bull Rev

Journal ID (iso-abbrev): Psychon Bull Rev

Title: Psychonomic Bulletin & Review

Publisher: Springer US (New York )

ISSN (Print): 1069-9384

ISSN (Electronic): 1531-5320

Publication date (Electronic): 9 October 2020

Publication date PMC-release: 9 October 2020

Publication date (Print): 2021

Volume: 28

Issue: 3

Pages: 813-826

Affiliations

[1 ]GRID grid.7177.6, ISNI 0000000084992262, University of Amsterdam, ; Amsterdam, Netherlands

[2 ]GRID grid.449564.e, ISNI 0000 0004 0501 5199, Nyenrode Business University, ; Breukelen, Netherlands

[3 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, University of California, ; Irvine, California USA

[4 ]GRID grid.6054.7, ISNI 0000 0004 0369 4183, Centrum Wiskunde & Informatica, ; Amsterdam, Netherlands

[5 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford University, ; Stanford, California USA

Article

Publisher ID: 1798

DOI: 10.3758/s13423-020-01798-5

PMC ID: 8219590

PubMed ID: 33037582

SO-VID: 63c366c6-e61e-4c12-812f-92ca4cb8c0f1

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.