IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v4y2020i6d10.1038_s41562-019-0807-z.html
   My bibliography  Save this article

Discussion points for Bayesian inference

Author

Listed:
  • Balazs Aczel

    (ELTE, Eötvös Loránd University)

  • Rink Hoekstra

    (University of Groningen)

  • Andrew Gelman

    (Columbia University)

  • Eric-Jan Wagenmakers

    (University of Amsterdam)

  • Irene G. Klugkist

    (Utrecht University, Utrecht)

  • Jeffrey N. Rouder

    (University of California Irvine)

  • Joachim Vandekerckhove

    (University of California Irvine)

  • Michael D. Lee

    (University of California Irvine)

  • Richard D. Morey

    (University of Cardiff)

  • Wolf Vanpaemel

    (University of Leuven)

  • Zoltan Dienes

    (University of Sussex)

  • Don van Ravenzwaaij

    (University of Groningen)

Abstract

Why is there no consensual way of conducting Bayesian analyses? We present a summary of agreements and disagreements of the authors on several discussion points regarding Bayesian inference. We also provide a thinking guideline to assist researchers in conducting Bayesian inference in the social and behavioural sciences.

Suggested Citation

  • Balazs Aczel & Rink Hoekstra & Andrew Gelman & Eric-Jan Wagenmakers & Irene G. Klugkist & Jeffrey N. Rouder & Joachim Vandekerckhove & Michael D. Lee & Richard D. Morey & Wolf Vanpaemel & Zoltan Diene, 2020. "Discussion points for Bayesian inference," Nature Human Behaviour, Nature, vol. 4(6), pages 561-563, June.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:6:d:10.1038_s41562-019-0807-z
    DOI: 10.1038/s41562-019-0807-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-019-0807-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41562-019-0807-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eric-Jan Wagenmakers & Alexandra Sarafoglou & Sil Aarts & Casper Albers & Johannes Algermissen & Štěpán Bahník & Noah Dongen & Rink Hoekstra & David Moreau & Don Ravenzwaaij & Aljaž Sluga & Franziska , 2021. "Seven steps toward more transparency in statistical practice," Nature Human Behaviour, Nature, vol. 5(11), pages 1473-1480, November.
    2. Heckelei, Thomas & Huettel, Silke & Odening, Martin & Rommel, Jens, 2021. "The replicability crisis and the p-value debate – what are the consequences for the agricultural and food economics community?," Discussion Papers 316369, University of Bonn, Institute for Food and Resource Economics.
    3. John K. Kruschke, 2021. "Bayesian Analysis Reporting Guidelines," Nature Human Behaviour, Nature, vol. 5(10), pages 1282-1291, October.
    4. Kelter, Riko, 2022. "Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    5. Ho, Manh-Toan & La, Viet-Phuong & Nguyen, Minh-Hoang & Pham, Thanh-Hang & Vuong, Thu-Trang & Vuong, Ha-My & Pham, Hung-Hiep & Hoang, Anh-Duc & Vuong, Quan-Hoang, 2020. "An analytical view on STEM education and outcomes: Examples of the social gap and gender disparity in Vietnam," Children and Youth Services Review, Elsevier, vol. 119(C).
    6. Mantello, Peter & Ho, Tung Manh & Nguyen, Minh-Hoang & Vuong, Quan-Hoang, 2021. "My Boss the Computer: A Bayesian analysis of socio-demographic and cross-cultural determinants of attitude toward the Non-Human Resource Management," OSF Preprints 4exjs, Center for Open Science.
    7. Fergus G Neville & John Drury & Stephen D Reicher & Sanjeedah Choudhury & Clifford Stott & Roger Ball & Daniel C Richardson, 2020. "Self-categorization as a basis of behavioural mimicry: Experiments in The Hive," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nathum:v:4:y:2020:i:6:d:10.1038_s41562-019-0807-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.