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New Semantics for Bayesian Inference: The Interpretive Problem and Its Solutions

Published online by Cambridge University Press:  01 January 2022


Scientists often study hypotheses that they know to be false. This creates an interpretive problem for Bayesians because the probability assigned to a hypothesis is typically interpreted as the probability that the hypothesis is true. I argue that solving the interpretive problem requires coming up with a new semantics for Bayesian inference. I present and contrast two new semantic frameworks, and I argue that both of them support the claim that there is pervasive pragmatic encroachment on whether a given Bayesian probability assignment is rational.

Research Article
Copyright © The Philosophy of Science Association

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I am grateful to audiences at the 2016 meeting of the Philosophy of Science Association, the University of Wisconsin–Madison, and Nanyang Technological University. I am indebted to conversations with Kenny Easwaran, Andrew Forcehimes, Andrea Guardo, and Reuben Stern, in particular, and I am especially thankful to Malcolm Forster, Elliott Sober, Jan Sprenger, and Mike Titelbaum for reading and giving comments on an earlier draft of the article. Research for this article was supported by Nanyang Technological University Start-Up Grant M4082134.


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