A curvature-weighted, non-flat volume measure removes the leading-order marginalization bias in posterior means, recovering cosmological parameters in mocks to below 0.1 sigma.
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Debiasing inference in large-scale structure with non-flat volume measures
A curvature-weighted, non-flat volume measure removes the leading-order marginalization bias in posterior means, recovering cosmological parameters in mocks to below 0.1 sigma.