Jeffrey Meets Kolmogorov: A General Theory of Conditioning.
Jeffrey conditionalization is a rule for updating degrees of belief in light of uncertain evidence. It is usually assumed that the partitions involved in Jeffrey conditionalization are finite and only contain positive-credence elements. But there are interesting examples, involving continuous quanti...
| Publicado en: | Journal of Philosophical Logic Vol. 49; no. 5; pp. 941 - 980 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Oct2020
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=145757769&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 145757769 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00223611 JPH jtl: Journal of Philosophical Logic issn: 00223611 maglogo: N pubinfo: dt: Oct2020 vid: 49 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 145757769 10.1007/s10992-019-09543-7 ppf: 941 ppct: 39 formats: fmt: @attributes: type: P size: 908KB tig: atl: Jeffrey Meets Kolmogorov: A General Theory of Conditioning. aug: au: Meehan, Alexander Zhang, Snow affil: Department of Philosophy, Princeton University, 1879 Hall, 08544, Princeton, NJ, USA su: Conditional probability Conditionals (Logic) sug: subj: Conditional probability Conditionals (Logic) keyword: Conditional distributions Jeffrey conditionalization Kolmogorovian conditionalization Probability-zero conditioning ab: Jeffrey conditionalization is a rule for updating degrees of belief in light of uncertain evidence. It is usually assumed that the partitions involved in Jeffrey conditionalization are finite and only contain positive-credence elements. But there are interesting examples, involving continuous quantities, in which this is not the case. Q1 Can Jeffrey conditionalization be generalized to accommodate continuous cases? Meanwhile, several authors, such as Kenny Easwaran and Michael Rescorla, have been interested in Kolmogorov's theory of regular conditional distributions (rcds) as a possible framework for conditional probability which handles probability-zero events. However the theory faces a major shortcoming: it seems messy and ad hoc. Q2 Is there some axiomatic theory which would justify and constrain the use of rcds, thus serving as a possible foundation for conditional probability? These two questions appear unrelated, but they are not, and this paper answers both. We show that when one appropriately generalizes Jeffrey conditionalization as in Q1, one obtains a framework which necessitates the use of rcds. It is then a short step to develop a general theory which addresses Q2, which we call the theory of extensions. The theory is a formal model of conditioning which recovers Bayesian conditionalization, Jeffrey conditionalization, and conditionalization via rcds as special cases. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Journal of Philosophical Logic is a copyright of Springer, 2020. All Rights Reserved. item: Journal of Philosophical Logic holder: Springer Nature dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
|---|