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AN INTUITIVE MOTIVATION OF BAYESIAN BELIEF MODELS
Author(s) -
Snow Paul
Publication year - 1995
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1995.tb00044.x
Subject(s) - axiom , set (abstract data type) , simple (philosophy) , bayesian probability , mathematics , representation (politics) , computer science , imprecise probability , axiomatic system , mathematical economics , artificial intelligence , probability distribution , statistics , epistemology , philosophy , geometry , politics , political science , law , programming language
The general use of subjective probabilities to model beliefs has been justified using many axiomatic schemes. This paper presents a rationale for probability models based on intuitive properties of belief orderings and the effect of evidence on beliefs. Qualitative probability, which imposes stringent constraints on belief representation schemes, is derived from four simple assumptions about beliefs and evidence. Properties shown to be sufficient for the adoption of probability proper by Cox (1978) are derived here from qualitative probability and a principle of plausible reasoning advanced by Polya (1954). Models based on complete orderings of beliefs extend easily to motivate set‐valued representations of partial orderings as well.