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2022 | OriginalPaper | Chapter

Valid Inferential Models Offer Performance and Probativeness Assurances

Authors : Leonardo Cella, Ryan Martin

Published in: Belief Functions: Theory and Applications

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter On Conditional Belief Functions in the Dempster-Shafer Theory

next chapter A Qualitative Counterpart of Belief Functions with Application to Uncertainty Propagation in Safety Cases

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Title: Valid Inferential Models Offer Performance and Probativeness Assurances
Authors: Leonardo Cella
Ryan Martin
Publisher: Springer International Publishing
Book: Belief Functions: Theory and Applications
Print ISBN: 978-3-031-17800-9

Electronic ISBN: 978-3-031-17801-6

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-031-17801-6_21