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Bayesian Philosophy of Science$
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Jan Sprenger and Stephan Hartmann

Print publication date: 2019

Print ISBN-13: 9780199672110

Published to Oxford Scholarship Online: October 2019

DOI: 10.1093/oso/9780199672110.001.0001

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Models, Idealizations and Objective Chance

Models, Idealizations and Objective Chance

Chapter:
(p.311) Variation 12: Models, Idealizations and Objective Chance
Source:
Bayesian Philosophy of Science
Author(s):

Jan Sprenger

Stephan Hartmann

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199672110.003.0012

How does Bayesian inference handle the highly idealized nature of many (statistical) models in science? The standard interpretation of probability as degree of belief in the truth of a model does not seem to apply in such cases since all candidate models are most probably wrong. Similarly, it is not clear how chance-credence coordination works for the probabilities generated by a statistical model. We solve these problems by developing a suppositional account of degree of belief where probabilities in scientific modeling are decoupled from our actual (unconditional) degrees of belief. This explains the normative pull of chance-credence coordination in Bayesian inference, uncovers the essentially counterfactual nature of reasoning with Bayesian models, and squares well with our intuitive judgment that statistical models provide “objective” probabilities.

Keywords:   statistical models, idealizations, Bayesian inference, chance-credence coordination, Principal Principle, suppositional account of degrees of belief

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