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  1. The theory of probability.Hans Reichenbach - 1949 - Berkeley,: University of California Press.
    We must restrict to mere probability not only statements of comparatively great uncertainty, like predictions about the weather, where we would cautiously ...
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  • Induction and hypothesis.Stephen Francis Barker - 1957 - Ithaca, N.Y.,: Cornell University Press.
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  • Forster and Sober on the curve-fitting problem.André Kukla - 1995 - British Journal for the Philosophy of Science 46 (2):248-252.
    Forster and Sober present a solution to the curve-fitting problem based on Akaike's Theorem. Their analysis shows that the curve with the best epistemic credentials need not always be the curve that most closely fits the data. However, their solution does not, without further argument, avoid the two difficulties that are traditionally associated with the curve-fitting problem: that there are infinitely many equally good candidate-curves relative to any given set of data, and that these best candidates include curves with indefinitely (...)
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  • Akaike information criterion, curve-fitting, and the philosophical problem of simplicity.I. A. Kieseppä - 1997 - British Journal for the Philosophy of Science 48 (1):21-48.
    The philosophical significance of the procedure of applying Akaike Information Criterion (AIC) to curve-fitting problems is evaluated. The theoretical justification for using AIC (the so-called Akaike's theorem) is presented in a rigorous way, and its range of validity is assessed by presenting both instances in which it is valid and counter-examples in which it is invalid. The philosophical relevance of the justification that this result gives for making one particular choice between simple and complicated hypotheses is emphasized. In addition, recent (...)
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  • The reference class problem is your problem too.Alan Hájek - 2007 - Synthese 156 (3):563--585.
    The reference class problem arises when we want to assign a probability to a proposition (or sentence, or event) X, which may be classified in various ways, yet its probability can change depending on how it is classified. The problem is usually regarded as one specifically for the frequentist interpretation of probability and is often considered fatal to it. I argue that versions of the classical, logical, propensity and subjectivist interpretations also fall prey to their own variants of the reference (...)
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  • How to Tell When Simpler, More Unified, or Less A d Hoc Theories Will Provide More Accurate Predictions.Malcolm R. Forster & Elliott Sober - 1994 - British Journal for the Philosophy of Science 45 (1):1-35.
    Traditional analyses of the curve fitting problem maintain that the data do not indicate what form the fitted curve should take. Rather, this issue is said to be settled by prior probabilities, by simplicity, or by a background theory. In this paper, we describe a result due to Akaike [1973], which shows how the data can underwrite an inference concerning the curve's form based on an estimate of how predictively accurate it will be. We argue that this approach throws light (...)
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  • Evaluating extreme risks in invasion ecology: learning from banking compliance.James Franklin, Mark Burgman, Scott Sisson & J. K. Martin - 2008 - Diversity and Distributions 14:581-591.
    methods that have shown promise for improving extreme risk analysis, particularly for assessing the risks of invasive pests and pathogens associated with international trade. We describe the legally inspired regulatory regime for banks, where these methods have been brought to bear on extreme ‘operational risks’. We argue that an ‘advocacy model’ similar to that used in the Basel II compliance regime for bank operational risks and to a lesser extent in biosecurity import risk analyses is ideal for permitting the diversity (...)
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  • Adaptive information and animal behaviour: Why motorists stop at red traffic lights.Ronald W. Templeton & James Franklin - 1992 - Evolutionary Theory 10:145-155.
    Argues that information, in the animal behaviour or evolutionary context, is correlation/covariation. The alternation of red and green traffic lights is information because it is (quite strictly) correlated with the times when it is safe to drive through the intersection; thus driving in accordance with the lights is adaptive (causative of survival). Daylength is usefully, though less strictly, correlated with the optimal time to breed. Information in the sense of covariance implies what is adaptive; if an animal can infer what (...)
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