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Specifying prior distributions in Bayesian analysis requires careful consideration. This study highlights key decisions in prior elicitation that significantly impact results, urging researchers to navigate these choices deliberately.

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Area of Science:

  • Statistics
  • Bayesian Inference

Background:

  • Bayesian statistical framework relies on prior distributions reflecting pre-existing knowledge.
  • Prior distributions significantly influence Bayesian analysis outcomes, necessitating careful specification.
  • Prior elicitation, using expert knowledge, is a common method for deriving priors.

Purpose of the Study:

  • To identify and categorize implicit decisions researchers make during prior elicitation.
  • To raise awareness of methodological flexibility and its impact on elicited priors.
  • To provide a structured approach for navigating decision paths in prior elicitation.

Main Methods:

  • Discussion of implicit decisions in prior elicitation.
  • Categorization of decisions into setup, core process, and expert combination.
  • Analysis of how different decision paths affect elicited priors.

Main Results:

  • Prior elicitation involves critical researcher decisions in setup, process, and expert combination.
  • Varying decision paths can lead to substantially different priors from the same experts.
  • Explicitly outlining these decisions enhances awareness of methodological flexibility.

Conclusions:

  • Researchers must carefully consider practical decisions throughout the prior elicitation process.
  • Awareness of methodological flexibility is crucial for robust Bayesian analyses.
  • Explicit decisions lay groundwork for evidence-based best practices in prior elicitation.