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Statistics > Methodology

arXiv:2011.01808 (stat)
[Submitted on 3 Nov 2020]

Title:Bayesian Workflow

Authors:Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C.
Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry,
Paul-Christian Burkner, Martin Modrak
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    Abstract: The Bayesian approach to data analysis provides a
    powerful way to handle uncertainty in all observations, model
    parameters, and model structure using probability theory.
    Probabilistic programming languages make it easier to specify and
    fit Bayesian models, but this still leaves us with many options
    regarding constructing, evaluating, and using these models, along
    with many remaining challenges in computation. Using Bayesian
    inference to solve real-world problems requires not only
    statistical skills, subject matter knowledge, and programming,
    but also awareness of the decisions made in the process of data
    analysis. All of these aspects can be understood as part of a
    tangled workflow of applied Bayesian statistics. Beyond
    inference, the workflow also includes iterative model building,
    model checking, validation and troubleshooting of computational
    problems, model understanding, and model comparison. We review
    all these aspects of workflow in the context of several examples,
    keeping in mind that in practice we will be fitting many models
    for any given problem, even if only a subset of them will
    ultimately be relevant for our conclusions.

Comments: 77 pages, 35 figures
Subjects: Methodology (stat.ME)
Cite as:  arXiv:2011.01808 [stat.ME]
          (or arXiv:2011.01808v1 [stat.ME] for this version)
          https://doi.org/10.48550/arXiv.2011.01808
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Daniel Simpson [view email]
[v1] Tue, 3 Nov 2020 15:59:50 UTC (7,801 KB)
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