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  1. Correlations, deviations and expectations: the Extended Principle of the Common Cause.Claudio Mazzola - 2013 - Synthese 190 (14):2853-2866.
    The Principle of the Common Cause is usually understood to provide causal explanations for probabilistic correlations obtaining between causally unrelated events. In this study, an extended interpretation of the principle is proposed, according to which common causes should be invoked to explain positive correlations whose values depart from the ones that one would expect to obtain in accordance to her probabilistic expectations. In addition, a probabilistic model for common causes is tailored which satisfies the generalized version of the principle, at (...)
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  • Counterexamples to a likelihood theory of evidence.Malcolm R. Forster - 2006 - Minds and Machines 16 (3):319-338.
    The likelihood theory of evidence (LTE) says, roughly, that all the information relevant to the bearing of data on hypotheses (or models) is contained in the likelihoods. There exist counterexamples in which one can tell which of two hypotheses is true from the full data, but not from the likelihoods alone. These examples suggest that some forms of scientific reasoning, such as the consilience of inductions (Whewell, 1858. In Novum organon renovatum (Part II of the 3rd ed.). The philosophy of (...)
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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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  • Unification, explanation, and the composition of causes in Newtonian mechanics.Malcolm R. Forster - 1988 - Studies in History and Philosophy of Science Part A 19 (1):55-101.
    William Whewell’s philosophy of scientific discovery is applied to the problem of understanding the nature of unification and explanation by the composition of causes in Newtonian mechanics. The essay attempts to demonstrate: the sense in which ”approximate’ laws successfully refer to real physical systems rather than to idealizations of them; why good theoretical constructs are not badly underdetermined by observation; and why, in particular, Newtonian forces are not conventional and how empiricist arguments against the existence of component causes, and against (...)
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  • Bayes and Bust: Simplicity as a Problem for a Probabilist’s Approach to Confirmation. [REVIEW]Malcolm R. Forster - 1995 - British Journal for the Philosophy of Science 46 (3):399-424.
    The central problem with Bayesian philosophy of science is that it cannot take account of the relevance of simplicity and unification to confirmation, induction, and scientific inference. The standard Bayesian folklore about factoring simplicity into the priors, and convergence theorems as a way of grounding their objectivity are some of the myths that Earman's book does not address adequately. 1Review of John Earman: Bayes or Bust?, Cambridge, MA. MIT Press, 1992, £33.75cloth.
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