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This study introduces a novel simulation-based method for prior elicitation in Bayesian statistics, effectively translating diverse expert knowledge into prior distributions for any model. The method proves robust and adaptable across various statistical models and elicitation techniques.

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Area of Science:

  • Statistics
  • Computational Statistics
  • Bayesian Inference

Background:

  • Bayesian statistics enables incorporating prior knowledge into models.
  • Prior elicitation translates domain expert knowledge into prior distributions.
  • Existing methods struggle to integrate diverse expert knowledge formats.

Purpose of the Study:

  • To develop a simulation-based prior elicitation method.
  • To effectively utilize diverse expert knowledge formats (data, statistics, parameters).
  • To formulate prior distributions aligned with expert expectations, model-independently.

Main Methods:

  • A simulation-based approach using stochastic gradient descent.
  • Learning hyperparameters for any parametric prior distribution.
  • Adaptable to quantile-based, moment-based, and histogram-based elicitation.

Main Results:

  • The method effectively learns prior distribution hyperparameters.
  • Demonstrated effectiveness and robustness across linear, generalized linear, and hierarchical models.
  • The approach is largely independent of the underlying model structure.

Conclusions:

  • The developed method offers a flexible and robust solution for prior elicitation.
  • It successfully integrates diverse forms of expert knowledge.
  • Applicable to a wide range of Bayesian modeling scenarios.