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Search: WFRF:(Kolflaath Eivind)

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  • Dahlman, Christian, et al. (author)
  • Causal models versus reason models in Bayesian networks for legal evidence
  • 2022
  • In: Synthese. - : Springer Science and Business Media LLC. - 1573-0964. ; 200:477
  • Journal article (peer-reviewed)abstract
    • In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models.
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  • Result 1-3 of 3
Type of publication
journal article (2)
book chapter (1)
Type of content
peer-reviewed (3)
Author/Editor
Dahlman, Christian (3)
Kolflaath, Eivind (3)
Stein, Alex (1)
Tuzet, Giovanni (1)
University
Lund University (3)
Language
English (3)
Research subject (UKÄ/SCB)
Social Sciences (3)
Humanities (1)

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