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September 10, 2025Open Access

Improper Priors via Expectation Measures

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Improper prior distributions can cause calculation issues in Bayesian inference, leading to potential errors.
Expectation measures offer a foundational approach to uncertainty, allowing for improved handling of improper priors.
Using expectation theory aids in deriving posterior distributions from improper prior distributions effectively.
This framework reduces reliance on approximations, enhancing the robustness of Bayesian statistical methods.

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This analysis shows how expectation measures enable proper posterior distributions from improper priors, suggesting new pathways for inference.

Peter Harremoës (2025) studied this question.

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This analysis shows how expectation measures enable proper posterior distributions from improper priors, suggesting new pathways for inference.