BAYESIAN AND NON-BAYESIAN EVIDENTIAL UPDATING

被引：136
|
作者：
KYBURG, HE
机构：
[1] Univ of Rochester, Rochester, NY,, USA, Univ of Rochester, Rochester, NY, USA
来源：
ARTIFICIAL INTELLIGENCE | 1987年 / 31卷 / 03期
关键词：
PROBABILITY;
D O I：
10.1016/0004-3702(87)90068-3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Four main results are arrived at in this paper (1) Closed convex sets of classical probability functions provide a representation of belief that includes the representations provided by Shafer probability mass functions as a special case. (2) The impact of 'uncertain evidence' can be (formally) represented by Dempster conditioning, in Shafer's framework. (3) The impact of 'uncertain evidence' can be (formally) represented in the framework of convex sets of classical probabilities by classical conditionalization. (4) The probability intervals that result from Dempster-Shafer updating on uncertain evidence are included in (and may be properly included in) the intervals that result from Bayesian updating on uncertain evidence.
引用
收藏
页码：271 / 293
页数：23
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