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Toward good practices for Bayesian data-rich fisheries stock assessments using a modern statistical workflow

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  • Journal Title:
    Fisheries Research
  • Personal Author:
    Monnahan, Cole C.
  • NOAA Program & Office:
  • Description:
    Bayesian inference has long been recognized as useful for fisheries stock assessments but it is less common than maximum likelihood approaches due to long run times and a lack of good practices. Recent computational advances leave developing good practices and user-friendly interfaces as the most important hurdles to wider use of this powerful statistical paradigm. Here, I argue that the modern Bayesian workflow proposed by Gelman et al. (2020) should form the basis for proposed good practices in fisheries sciences. Their workflow is a conceptual roadmap for iterative model building which includes the philosophical role of priors and how to apply statistical tools to construct them, how to validate and compare models, and how to overcome computational problems.
  • Source:
    Fisheries Research, 275, 107024
  • DOI:
  • ISSN:
    0165-7836
  • Format:
    PDF
  • Publisher:
    Elsevier BV
  • Document Type:
    Journal Article
  • Rights Information:
    Accepted Manuscript
  • Compliance:
    Submitted
  • Main Document Checksum:
    urn:sha-512:a5b4f90d806497177c5a54fce2ff08c425c147751f3f25c584b75810db5480b406d1a1923a29a2634571f5a79d54cb0c01f679ec7a7f0e07772431f6c49a34ce
  • Download URL:
  • File Type:
    Filetype[PDF - 2.15 MB ]
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