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Visualization in Bayesian workflow


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


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, 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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    Cited by:

    1. Felipe Maia Polo, 2020. "Skills to not fall behind in school," Papers 2001.10519,
    2. Lindeløv, Jonas Kristoffer, 2020. "mcp: An R Package for Regression With Multiple Change Points," OSF Preprints fzqxv, Center for Open Science.

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