Repository URL:
http://philsci-archive.pitt.edu/id/eprint/13515
Author(s):
Sprenger, Jan
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preprint description
This paper articulates and defends a suppositional interpretation of conditional degree of belief. First, I focus on a type of probability that has a crucial role in Bayesian inference: conditional degrees of belief in an observation, given a statistical hypothesis. The suppositional analysis explains, unlike other accounts, why these degrees of belief track the corresponding probability density functions. Then, I extend the suppositional analysis and argue that all probabilities in Bayesian inference should be understood suppositionally and model-relative. This sheds a new and illuminating light on chance-credence coordination principles, the relationship between Bayesian models and their target system, and the epistemic significance of Bayes' Theorem.

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