New Semantics for Bayesian Inference

The Interpretive Problem and Its Solutions

Bibliographic Data

ID10705239
AuthorsOlav Benjamin Vassend (0000-0002-5964-8835, corresponding author)
Year2019
Volume86
Issue4
Pages696-718
Publication date2019-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePhilosophy of Science (JOURNAL)
Journal identifiersISSN: 0031-8248 • E-ISSN: 1539-767X
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1086/704978
OpenAlexW2898926594
LanguageEN
Citations received3
References cited31

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

Bayesian inference · Bayesian probability · Bayesian statistics · Contrast (vision) · Epistemology · Frequentist probability · Inference · Mathematical economics · Programming language · Semantics (computer science) · Statistical inference · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Epistemology, Ethics, and Metaphysics · Mathematics · Philosophy · Philosophy and History of Science

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