Non-bayesian foundations for statistical estimation, prediction, and the ravens example

Erkenntnis 40 (3):357 - 376 (1994)
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Abstract

The paper provides a formal proof that efficient estimates of parameters, which vary as as little as possible when measurements are repeated, may be expected to provide more accurate predictions. The definition of predictive accuracy is motivated by the work of Akaike (1973). Surprisingly, the same explanation provides a novel solution for a well known problem for standard theories of scientific confirmation — the Ravens Paradox. This is significant in light of the fact that standard Bayesian analyses of the paradox fail to account for the predictive utility of universal laws like All ravens are black.

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Malcolm Forster
University of Wisconsin, Madison

Citations of this work

Hempel's Raven Paradox: A Lacuna in the Standard Bayesian Solution.Peter B. M. Vranas - 2004 - British Journal for the Philosophy of Science 55 (3):545-560.
Bayes' Theorem.James Joyce - 2008 - Stanford Encyclopedia of Philosophy.
A Gruesome Problem for the Curve-Fitting Solution.Scott DeVito - 1997 - British Journal for the Philosophy of Science 48 (3):391-396.

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