From Posterior Discrepancy to Inferential Consequences: A Comment on Sugasawa (2026)

Q: What does this research mean for the field?

The representation of Hellinger and Kullback-Leibler distances can be utilized to evaluate the inferential consequences of posterior discrepancies in prior sensitivity analysis without requiring model re-fitting. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jomel Mabborang; Riza Calimag; Kastin Joyce Ibay; Romnick D. Magusib

doi:10.1080/00031305.2026.2732103

September 8, 2026The American Statistician

From Posterior Discrepancy to Inferential Consequences: A Comment on Sugasawa (2026)

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Key Points

To examine how sample-based representations of posterior discrepancy metrics translate into concrete inferential consequences in Bayesian prior sensitivity analysis without model re-fitting.
Critically evaluated the mathematical formulations of Hellinger and Kullback–Leibler distances derived from posterior samples as proposed by Sugasawa (2026).
Assessed theoretical and practical linkages between abstract probability distribution divergences and actionable parameter inference.
Highlights that statistical divergence measures across posterior distributions do not uniformly reflect the magnitude of changes in specific point estimates or credible intervals.
Emphasizes the methodological necessity of mapping general discrepancy metrics directly to domain-specific decision criteria when evaluating prior sensitivity.

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Implication

Methodological commentary evaluates sample-based discrepancy metrics in Bayesian prior sensitivity analysis, highlighting the need to link distance measures to downstream inferential impact.

Abstract

Dear Editor,We read with interest Sugasawa’s (2026) article, Prior Sensitivity Analysis without Model Re-fit. The proposed representation of Hellinger and Kullback–Leibler (KL) distances using samp...

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Mabborang et al. (2026) studied this question.

synapsesocial.com/papers/6aa27b2658559d80afc743ac https://doi.org/10.1080/00031305.2026.2732103

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From Posterior Discrepancy to Inferential Consequences: A Comment on Sugasawa (2026)

Key Points

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From Posterior Discrepancy to Inferential Consequences: A Comment on Sugasawa (2026)

Key Points

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Implication

Abstract

Discussion

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