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  Automatic Derivation of Probabilistic Inference Rules

Jaeger, M. (2001). Automatic Derivation of Probabilistic Inference Rules. International Journal of Approximate Reasoning, 28(1), 1-22.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-31EB-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-31EC-9
Genre: Journal Article

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 Creators:
Jaeger, Manfred1, Author              
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1Programming Logics, MPI for Informatics, Max Planck Society, escidoc:40045              

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 Abstract: A probabilistic inference rule is a general rule that provides bounds on a target probability given constraints on a number of input probabilities. Example: from $P(A | B) \leq r$\ infer $P(\neg A | B) \in [1-r,1]$. Rules of this kind have been studied extensively as a deduction method for propositional probabilistic logics. Many different rules have been proposed, and their validity proved -- often with substantial effort. Building on previous work by T. Hailperin, in this paper we show that probabilistic inference rules can be derived automatically, i.e. given the input constraints and the target probability, one can automatically derive the optimal bounds on the target probability as a functional expression in the parameters of the input constraints.

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Language(s): eng - English
 Dates: 2010-03-122001
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: eDoc: 519819
Other: Local-ID: C1256104005ECAFC-84E804A247DFE550C1256AA9004EBC08-JaegerIJAR01
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Title: International Journal of Approximate Reasoning
Source Genre: Journal
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Pages: - Volume / Issue: 28 (1) Sequence Number: - Start / End Page: 1 - 22 Identifier: ISSN: 0888-613X