Representing credal imprecision: from sets of measures to hierarchical Bayesian models.
The basic Bayesian model of credence states, where each individual's belief state is represented by a single probability measure, has been criticized as psychologically implausible, unable to represent the intuitive distinction between precise and imprecise probabilities, and normatively unjustifiab...
| Publicado en: | Philosophical Studies Vol. 177; no. 6; pp. 1463 - 1486 |
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| Formato: | Artículo |
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Springer Nature
Jun2020
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| 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=142924982&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 142924982 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318116 4L8 jtl: Philosophical Studies issn: 00318116 maglogo: N pubinfo: dt: Jun2020 vid: 177 iid: 6 pid: 237 pub: Springer Nature artinfo: ui: 142924982 10.1007/s11098-019-01262-8 ppf: 1463 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P size: 421KB tig: atl: Representing credal imprecision: from sets of measures to hierarchical Bayesian models. aug: au: Lassiter, Daniel affil: Department of Linguistics, Stanford University, 450 Serra Mall, Building 460, 94305, Stanford, CA, USA su: Science Developmental psychology & motivation Learning Forensic orations Bayesian analysis sug: subj: Science Developmental psychology & motivation Learning Forensic orations Bayesian analysis keyword: Bayesian cognitive science Bayesian epistemology Bayesian networks Credal imprecision Hierarchical Bayesian models Philosophy of cognitive science Probability ab: The basic Bayesian model of credence states, where each individual's belief state is represented by a single probability measure, has been criticized as psychologically implausible, unable to represent the intuitive distinction between precise and imprecise probabilities, and normatively unjustifiable due to a need to adopt arbitrary, unmotivated priors. These arguments are often used to motivate a model on which imprecise credal states are represented by sets of probability measures. I connect this debate with recent work in Bayesian cognitive science, where probabilistic models are typically provided with explicit hierarchical structure. Hierarchical Bayesian models are immune to many classic arguments against single-measure models. They represent grades of imprecision in probability assignments automatically, have strong psychological motivation, and can be normatively justified even when certain arbitrary decisions are required. In addition, hierarchical models show much more plausible learning behavior than flat representations in terms of sets of measures, which—on standard assumptions about update—rule out simple cases of learning from a starting point of total ignorance. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Philosophical Studies is a copyright of Springer, 2020. All Rights Reserved. item: Philosophical Studies holder: Springer Nature dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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