Visualization in Bayesian Workflow
Jonah Gabry(Columbia University), Daniel Simpson(University of Toronto), Aki Vehtari(Aalto University), Michael Betancourt(Columbia University), Andrew Gelman(Columbia University)
Journal of the Royal Statistical Society Series A (Statistics in Society)
January 15, 2019
Cited by 1,122Open Access
Abstract
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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