No CrossRef data available.
Article contents
Supposition and (Statistical) Models
Published online by Cambridge University Press: 17 February 2023
Abstract
In a recent paper, Sprenger advances what he calls a “suppositional” answer to the question of why a Bayesian agent’s degrees of belief should align with the probabilities found in statistical models. I show that Sprenger’s account trades on an ambiguity between hypothetical and subjunctive suppositions and cannot succeed once we distinguish between the two.
- Type
- Discussion Note
- Information
- Copyright
- © The Author(s), 2023. Published by Cambridge University Press on behalf of the Philosophy of Science Association
References
Adams, Ernest. 1975. The Logic of Conditionals: An Application of Probability to Deductive Logic. Dordrecht: D. Reidel.CrossRefGoogle Scholar
Box, George E. P. 1976. “Science and Statistics.” Journal of the American Statistical Association 71 (356):791–99.CrossRefGoogle Scholar
Garber, Daniel. 1983. “Old Evidence and Logical Omniscience in Bayesian Confirmation Theory.” Minnesota Studies in the Philosophy of Science 10: 99–132.Google Scholar
Pearl, Judea. 2009. Causality: Models, Reasoning, and Inference. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ribes, Aurélien, Qasmi, Saïd, and Gillett, Nathan P.. 2021. “Making Climate Projections Conditional on Historical Observations.” Science Advances 7 (4):1–9.CrossRefGoogle ScholarPubMed
Schwarz, Wolfgang. 2018. “Subjunctive Conditional Probability.” Journal of Philosophical Logic 47: 47–66.CrossRefGoogle Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard. 2000. Causation, Prediction, and Search. 2nd ed. Cambridge, MA: MIT Press.Google Scholar
Sprenger, Jan. 2019. “Conditional Degree of Belief and Bayesian Inference.” Philosophy of Science 87 (2):319–35.CrossRefGoogle Scholar