Learn-merge invariance of priors: A characterization of the Dirichlet distributions and processes

DOI10.1016/0047-259X(86)90060-6zbMath0585.62003MaRDI QIDQ1070681

B. George

Publication date: 1986

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

inductive learningprior distributionsR. CarnapBayesian learning modelDirichlet distributions and processesLearn-merge invarianceprinciple of natural conjugate priorssymmetric measuresW. E. Johnson

Mathematics Subject Classification ID

Foundations and philosophical topics in statistics (62A01) Philosophical and critical aspects of logic and foundations (03A05) General logic (03B99) Nonparametric inference (62G99)

Related Items (3)

Characterizing Dirichlet Priors ⋮ The History of the Dirichlet and Liouville Distributions ⋮ The logical postulates of Böge, Carnap and Johnson in the context of Papangelou processes

Cites Work

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• W. E. Johnson's sufficientness postulate
• A Bayesian analysis of some nonparametric problems
• Symmetric Measures on Cartesian Products
• I.—PROBABILITY: THE DEDUCTIVE AND INDUCTIVE PROBLEMS
• Die Problematik apriorischer Wahrscheinlichkeiten im System der induktiven Logik von Rudolf Carnap

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