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Statistical inference and subjective probabilities
Author(s) -
Koerts J.,
Leede E. de
Publication year - 1973
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/j.1467-9574.1973.tb00222.x
Subject(s) - inference , interpretation (philosophy) , binomial distribution , statistical inference , artificial intelligence , mathematics , process (computing) , prior probability , computer science , machine learning , econometrics , statistics , bayesian probability , programming language , operating system
Summary “Learning by experience” is a well‐known part of the theory of subjective probabilities; the learning process is often derived from some prior distribution F(p ) where p is a parameter of unknown value of a binomial process for instance. In this paper, the learning process is explicitly formulated and the corresponding prior distribution is derived from it. In this interpretation, subjective probabilities are part of an inference methodology, rather than a subjective evaluation of frequentistic probabilities. Implications are considered for a concept like the “non‐informative prior; the situation is considered in which the learning process seems to be in contact with some objectively determined prior.