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Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

Grim, Patrick and Seidl, Frank and McNamara, Calum and Rago, Hinton and Astor, Isabell and Diaso, Caroline and Ryner, Peter (2021) Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity. [Preprint]

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Abstract

We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Grim, Patrickpatrick.grim@stonybrook.edu
Seidl, Frankfcseid@umich.edu
McNamara, Calumcamcnam@umich.edu
Rago, Hintonragohin@umich.edu
Astor, Isabellastori@umich.edu
Diaso, Carolinecdiaso@umich.edu
Ryner, Peterrynerp@umich.edu
Additional Information: Forthcoming in Philosophy of Science
Keywords: Bayesian networks, scientific theories, confirmation, evidence, sensitivity
Subjects: General Issues > Confirmation/Induction
General Issues > Structure of Theories
General Issues > Theory Change
General Issues > Theory/Observation
Depositing User: Patrick Grim
Date Deposited: 10 Feb 2021 15:47
Last Modified: 10 Feb 2021 15:47
Item ID: 18705
Subjects: General Issues > Confirmation/Induction
General Issues > Structure of Theories
General Issues > Theory Change
General Issues > Theory/Observation
Date: 2021
URI: http://philsci-archive.pitt.edu/id/eprint/18705

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