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Visualization in Bayesian workflow

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arxiv 1709.01449 v5 pith:QJXIQUHB submitted 2017-09-05 stat.ME stat.AP

Jonah Gabry , Daniel Simpson , Aki Vehtari , Michael Betancourt , Andrew Gelman This is my paper · ORCID
classification stat.MEstat.AP
keywords bayesiananalysisdatamodelvisualizationworkflowappliedbuilding
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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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Cited by 1 Pith paper

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  1. Posterior SBC: Simulation-Based Calibration Checking Conditional on Data

    stat.ME 2025-02 conditional novelty 6.0 of 10

    Posterior SBC validates Bayesian inference conditional on observed data by treating the posterior as the reference distribution and testing calibration of augmented posteriors.

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