IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v182y2019i2p389-402.html
   My bibliography  Save this article

Visualization in Bayesian workflow

Author

Listed:
  • Jonah Gabry
  • Daniel Simpson
  • Aki Vehtari
  • Michael Betancourt
  • Andrew Gelman

Abstract

Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

Suggested Citation

  • Jonah Gabry & Daniel Simpson & Aki Vehtari & Michael Betancourt & Andrew Gelman, 2019. "Visualization in Bayesian workflow," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 389-402, February.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:2:p:389-402
    DOI: 10.1111/rssa.12378
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12378
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12378?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
    ---><---

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Sam Watson’s journal round-up for 11th February 2019
      by Sam Watson in The Academic Health Economists' Blog on 2019-02-11 13:43:11

    Citations

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


    Cited by:

    1. Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.
    2. 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.
    3. Zhang, Yunchang & Fricker, Jon D., 2021. "Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model," Transport Policy, Elsevier, vol. 103(C), pages 11-20.
    4. Felipe Maia Polo, 2020. "Skills to not fall behind in school," Papers 2001.10519, arXiv.org.
    5. Barakat, Bilal Fouad & Dharamshi, Ameer & Alkema, Leontine & Antoninis, Manos, 2021. "Adjusted Bayesian Completion Rates (ABC) Estimation," SocArXiv at368, Center for Open Science.
    6. Andrea L Liebl & Jeff S Wesner & Andrew F Russell & Aaron W Schrey, 2021. "Methylation patterns at fledging predict delayed dispersal in a cooperatively breeding bird," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-13, June.
    7. Brian Hartley, 2022. "Episodic incidence of Harrodian instability and the Kaleckian growth model: A Markov‐switching approach," Metroeconomica, Wiley Blackwell, vol. 73(1), pages 268-290, February.
    8. Andrew J Tanentzap & Samuel Cottingham & Jérémy Fonvielle & Isobel Riley & Lucy M Walker & Samuel G Woodman & Danai Kontou & Christian M Pichler & Erwin Reisner & Laurent Lebreton, 2021. "Microplastics and anthropogenic fibre concentrations in lakes reflect surrounding land use," PLOS Biology, Public Library of Science, vol. 19(9), pages 1-18, September.
    9. Jair Andrade & Jim Duggan, 2021. "A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo," System Dynamics Review, System Dynamics Society, vol. 37(4), pages 283-309, October.
    10. Ameer Dharamshi & Bilal Barakat & Leontine Alkema & Manos Antoninis, 2022. "A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1822-1864, November.
    11. Karan Bhuwalka & Eunseo Choi & Elizabeth A. Moore & Richard Roth & Randolph E. Kirchain & Elsa A. Olivetti, 2023. "A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 43-55, February.
    12. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    13. Wagenmakers, Eric-Jan & Sarafoglou, Alexandra & Aarts, Sil Dr. & Albers, Casper J & Algermissen, Johannes & Bahník, Štěpán & van Dongen, Noah N'Djaye Nikolai & Hoekstra, Rink & Moreau, David & van Rav, 2021. "Toward More Transparency in Statistical Practice," MetaArXiv t93cg, Center for Open Science.
    14. Matthias Kloft & Raphael Hartmann & Andreas Voss & Daniel W. Heck, 2023. "The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 888-916, September.
    15. Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2023. "The tenets of quantile-based inference in Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    16. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
    17. Schwoerer, Tobias & Schmidt, Jennifer I. & Holen, Davin, 2020. "Predicting the Food-Energy Nexus of Wild Food Systems: Informing Energy Transitions for Isolated Indigenous Communities," Ecological Economics, Elsevier, vol. 176(C).
    18. Yu-Fang Chien & Haiming Zhou & Timothy Hanson & Theodore Lystig, 2023. "Informative g -Priors for Mixed Models," Stats, MDPI, vol. 6(1), pages 1-23, January.
    19. Antony Andrews & Sean Kimpton, 2023. "Econometric Forecasting of Tourist Arrivals Using Bayesian Structural Time‐Series," Economic Papers, The Economic Society of Australia, vol. 42(2), pages 200-211, June.
    20. Nater, Chloé Rebecca & Burgess, Malcolm D. & Coffey, Peter & Harris, Bob & Lander, Frank & Price, David & Reed, Mike & Robinson, Rob, 2022. "Multi-population analysis reveals spatial consistency in drivers of population dynamics of a declining migratory bird," EcoEvoRxiv 5ru9f, Center for Open Science.
    21. Aldo Gardini & Enrico Fabrizi & Carlo Trivisano, 2022. "Poverty and inequality mapping based on a unit‐level log‐normal mixture model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2073-2096, October.
    22. Frederik Banis & Henrik Madsen & Niels K. Poulsen & Daniela Guericke, 2020. "Prosumer Response Estimation Using SINDyc in Conjunction with Markov-Chain Monte-Carlo Sampling," Energies, MDPI, vol. 13(12), pages 1-16, June.

    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:bla:jorssa:v:182:y:2019:i:2:p:389-402. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.