Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

Philosophy of Science (forthcoming)
Download Edit this record How to cite View on PhilPapers
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.
PhilPapers/Archive ID
GRISTA-5
Upload history
Archival date: 2021-02-10
View other versions
Added to PP index
2021-02-10

Total views
102 ( #45,021 of 64,012 )

Recent downloads (6 months)
39 ( #19,803 of 64,012 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.