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October 11, 2025StatsOpen Access

Improper Priors via Expectation Measures

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Improper prior distributions complicate calculations in Bayesian inference, as they cannot be normalized.
Expectation measures provide an alternative foundation, potentially circumventing problems associated with improper priors.
Using point processes and expectation theory allows for the probabilistic interpretation of improper priors.
This approach may enable the calculation of posterior distributions without needing approximation methods.

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This research reveals issues of improper prior distributions in Bayesian statistics, suggesting expectation measures as a solution.

Peter Harremoës (2025) studied this question.

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