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Valid Inferential Models Offer Performance and Probativeness Assurances

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Belief Functions: Theory and Applications (BELIEF 2022)

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Bayesians and frequentists are now largely focused on developing methods that perform well in a frequentist sense. But the widely-publicized replication crisis suggests that performance guarantees are not enough for good science. In addition to reliably detecting hypotheses that are incompatible with data, users require methods that can probe for hypotheses that are actually supported by the data. In this paper, we demonstrate that valid inferential models achieve both performance and probativeness properties. We also draw important connections between inferential models and Deborah Mayo’s severe testing.

R. Martin—Partially supported by the U.S. National Science Foundation, SES–205122.

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Correspondence to Leonardo Cella .

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Cella, L., Martin, R. (2022). Valid Inferential Models Offer Performance and Probativeness Assurances. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham.

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  • Print ISBN: 978-3-031-17800-9

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