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Bayes rules!
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
2000 (English)Conference paper (Refereed)
Abstract [en]

Of the many justifications of Bayesianism, most imply some assumption that is not very compelling, like the differentiability or continuity of some auxiliary function. We show how such assumptions can be replaced by weaker assumptions for finite domains. The new assumptions are a non-informative refinement principle and a concept of information independence. These assumptions are weaker than those used in alternative justifications, which is shown by their inadequacy for infinite domains. They are also more compelling. 1 Introduction The normative claim of Bayesianism is that every type of uncertainty should be described as probability. Bayesianism has been quite controversial in both the statistics and the uncertainty management communities. It developed as subjective Bayesianism, in [5, 11]. Recently, the information based family of justifications, initiated in [3] and continued in [1] have been discussed in [12, 6, 13]. We will try to find assumptions that are strong enough to s...

Place, publisher, year, edition, pages
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-58729OAI: diva2:473857
ECCAI 2000, Berlin
NR 20140805Available from: 2012-01-08 Created: 2012-01-08Bibliographically approved

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Arnborg, Stefan
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Numerical Analysis and Computer Science, NADA
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