On the Modal Logic of Jeffrey Conditionalization

被引:3
作者
Gyenis, Zalan [1 ]
机构
[1] Jagiellonian Univ, Dept Log, Krakow, Poland
关键词
Modal logic; Bayesian inference; Bayes learning; Bayes logic; Jeffrey learning; Jeffrey conditionalization; PROBABILITY;
D O I
10.1007/s11787-018-0205-8
中图分类号
B81 [逻辑学(论理学)];
学科分类号
010104 ; 010105 ;
摘要
We continue the investigations initiated in the recent papers (Brown et al. in The modal logic of Bayesian belief revision, 2017; Gyenis in Standard Bayes logic is not finitely axiomatizable, 2018) where Bayes logics have been introduced to study the general laws of Bayesian belief revision. In Bayesian belief revision a Bayesian agent revises (updates) his prior belief by conditionalizing the prior on some evidence using the Bayes rule. In this paper we take the more general Jeffrey formula as a conditioning device and study the corresponding modal logics that we call Jeffrey logics, focusing mainly on the countable case. The containment relations among these modal logics are determined and it is shown that the logic of Bayes and Jeffrey updating are very close. It is shown that the modal logic of belief revision determined by probabilities on a finite or countably infinite set of elementary propositions is not finitely axiomatizable. The significance of this result is that it clearly indicates that axiomatic approaches to belief revision might be severely limited.
引用
收藏
页码:351 / 374
页数:24
相关论文
共 29 条
[1]   ON THE LOGIC OF THEORY CHANGE - PARTIAL MEET CONTRACTION AND REVISION FUNCTIONS [J].
ALCHOURRON, CE ;
GARDENFORS, P ;
MAKINSON, D .
JOURNAL OF SYMBOLIC LOGIC, 1985, 50 (02) :510-530
[2]  
[Anonymous], REPORTS MATH LOGIC
[3]  
[Anonymous], 2007, Journal of applied non-classical logics., DOI [10.3166/jancl.17.129-155, DOI 10.3166/JANCL.17.129-155]
[4]  
[Anonymous], 1996, ERKENNTNIS
[5]  
[Anonymous], BAYES BLIND SP UNPUB
[6]  
[Anonymous], STUDIA LOGICA
[7]  
azarz M, 2013, B SECT LOG, V42, P83
[8]  
Bacchus F., 1990, ECAI 90. Proceedings of the 9th European Conference on Artificial Intelligence, P59
[9]  
BARBER D., 2012, Bayesian Reasoning and Machine Learning
[10]  
BILLINGSLEY P., 1995, Probability and measure, V3rd