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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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Learning Conditional Evidence

Learning Conditional Evidence

Chapter:
(p.107) Variation 4: Learning Conditional Evidence
Source:
Bayesian Philosophy of Science
Author(s):

Jan Sprenger

Stephan Hartmann

Publisher:
Oxford University Press
DOI:10.1093/oso/9780199672110.003.0004

Learning indicative conditionals and learning relative frequencies have one thing in common: they are examples of conditional evidence, that is, evidence that includes a suppositional element. Standard Bayesian theory does not describe how such evidence affects rational degrees of belief, and natural solutions run into major problems. We propose that conditional evidence is best modeled by a combination of two strategies: First, by generalizing Bayesian Conditionalization to minimizing an appropriate divergence between prior and posterior probability distribution. Second, by representing the relevant causal relations and the implied conditional independence relations in a Bayesian network that constrains both prior and posterior. We show that this approach solves several well-known puzzles about learning conditional evidence (e.g., the notorious Judy Benjamin problem) and that learning an indicative conditional can often be described adequately by conditionalizing on the associated material conditional.

Keywords:   conditional evidence, indicative conditionals, relative frequencies, causal modeling, Bayesian Conditionalization, Judy Benjamin problem, method of divergence minimization

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