In:
Philosophy of Science, Cambridge University Press (CUP), Vol. 89, No. 1 ( 2022-01), p. 42-69
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.
Type of Medium:
Online Resource
ISSN:
0031-8248
,
1539-767X
Language:
English
Publisher:
Cambridge University Press (CUP)
Publication Date:
2022
detail.hit.zdb_id:
2066891-0
SSG:
11
SSG:
19,2
SSG:
5,1