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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
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    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

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    1. 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.
    2. 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.
    3. 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.
    4. Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Felipe Maia Polo, 2020. "Skills to not fall behind in school," Papers 2001.10519, arXiv.org.
    13. Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2023. "The tenets of quantile-based inference in Bayesian models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    14. 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.
    15. 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.
    16. Barakat, Bilal Fouad & Dharamshi, Ameer & Alkema, Leontine & Antoninis, Manos, 2021. "Adjusted Bayesian Completion Rates (ABC) Estimation," SocArXiv at368, Center for Open Science.
    17. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.
    18. 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.
    19. 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.
    20. 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).
    21. Bernard Baffour & Sumonkanti Das & Mu Li & Alice Richardson, 2024. "The Utility of Socioeconomic and Remoteness Indicators in Understanding the Geographical Variation in the Regional Prevalence of Early Childhood Vulnerability in Australia," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 17(4), pages 1791-1827, August.
    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.
    23. 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.

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