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.
| Original language | English |
|---|---|
| Pages (from-to) | 389-402 |
| Number of pages | 14 |
| Journal | Journal of the Royal Statistical Society. Series A: Statistics in Society |
| Volume | 182 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Feb 2019 |
| MoE publication type | A1 Journal article-refereed |
Funding
The authors thank Gavin Shaddick and Matthew Thomas for their help with the PM2:5 example, Ari Hartikainen for suggesting the parallel co-ordinates plot, Ghazal Fazelnia for finding an error in our map of ground monitor locations, Eren Metin Elc¸i for alerting us to a discrepancy between our text and code, and the Sloan Foundation, Columbia University, US National Science Foundation, Institute for Education Sciences, Office of Naval Research and Defense Advanced Research Projects Agency for financial support.
Keywords
- Bayesian data analysis
- Statistical graphics
- Statistical workflow
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