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On the foundations of bayesianism
Number of Authors: 2
2001 (English)Conference paper, (Refereed)
Abstract [en]

We discuss precise assumptions entailing Bayesianism in the line of investigations started by Cox, and relate them to a recent critique by Halpern. We show that every finite model which cannot be rescaled to probability violates a natural and simple refinability principle. A new condition, separability, was found sufficient and necessary for rescalability of infinite models.We finally characterise the acceptable ways to handle uncertainty in infinite models based on Cox's assumptions. Certain closure properties must be assumed before all the axioms of ordered fields are satisfied. Once this is done, a proper plausibility model can be embedded in an ordered field containing the reals, namely either standard probability (field of reals) for a real valued plausibility model, or extended probability (field of reals and infinitesimals) for an ordered plausibility model.

Place, publisher, year, edition, pages
2001, 1. , 10 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-22588ISBN: 0-7354-0004-0 (print)OAI: oai:DiVA.org:ri-22588DiVA: diva2:1042153
Conference
Twentieth International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Note
Published by Springer. ISBN 0-7354-0004-0.Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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