Visualization in Bayesian workflow

Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, Andrew Gelman
9/5/2017
stat.MEstat.AP

Official code: jgabry/bayes-vis-paper (68 stars, last commit 2020-10-22).

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.

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Code Implementations(1)

jgabry/bayes-vis-paperOfficial100%

'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)

6816R, TeXFeb 23, 20186 years ago
bayesian-data-analysisjournal-articlerstanstatistical-graphics+1 more

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Cite this paper

@article{gabry2017visualization,
  title  = {Visualization in Bayesian workflow},
  author = {Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman},
  year   = {2017},
  eprint = {1709.01449},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1709.01449}
}

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