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  1. Analogical Predictions for Explicit Similarity.Jan Willem Romeijn - 2006 - Erkenntnis 64 (2):253 - 280.
    This paper concerns exchangeable analogical predictions based on similarity relations between predicates, and deals with a restricted class of such relations. It describes a system of Carnapian λγ rules on underlying predicate families to model the analogical predictions for this restricted class. Instead of the usual axiomatic definition, the system is characterized with a Bayesian model that employs certain statistical hypotheses. Finally the paper argues that the Bayesian model can be generalized to cover cases outside the restricted class of similarity (...)
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  • De Finetti's earliest works on the foundations of probability.Jan Plato - 1989 - Erkenntnis 31 (2-3):263-282.
    Bruno de Finetti's earliest works on the foundations of probability are reviewed. These include the notion of exchangeability and the theory of random processes with independent increments. The latter theory relates to de Finetti's ideas for a probabilistic science more generally. Different aspects of his work are united by his foundational programme for a theory of subjective probabilities.
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  • The Concept of Inductive Probability.Patrick Maher - 2006 - Erkenntnis 65 (2):185-206.
    The word ‘probability’ in ordinary language has two different senses, here called inductive and physical probability. This paper examines the concept of inductive probability. Attempts to express this concept in other words are shown to be either incorrect or else trivial. In particular, inductive probability is not the same as degree of belief. It is argued that inductive probabilities exist; subjectivist arguments to the contrary are rebutted. Finally, it is argued that inductive probability is an important concept and that it (...)
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  • Probabilism and induction.Richard Jeffrey - 1986 - Topoi 5 (1):51-58.
  • Decoupling, Sparsity, Randomization, and Objective Bayesian Inference.Julio Michael Stern - 2008 - Cybernetics and Human Knowing 15 (2):49-68..
    Decoupling is a general principle that allows us to separate simple components in a complex system. In statistics, decoupling is often expressed as independence, no association, or zero covariance relations. These relations are sharp statistical hypotheses, that can be tested using the FBST - Full Bayesian Significance Test. Decoupling relations can also be introduced by some techniques of Design of Statistical Experiments, DSEs, like randomization. This article discusses the concepts of decoupling, randomization and sparsely connected statistical models in the epistemological (...)
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  • Construtivismo cognitivo e estatística bayesiana.Julio Michael Stern - 2006 - Scientiae Studia 4 (4):598-613.
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  • Hidden determinism, probability, and time's arrow.Hans Primas - unknown
    In present-day physics the fundamental dynamical laws are taken as a time-translation-invariant and time-reversal-invariant one-parameter groups of automorphisms of the underlying mathematical structure. In this context-independent and empirically inaccessible description there is no past, present or future, hence no distinction between cause and effect. To get the familiar description in terms of causes and effects, the time-reversal symmetry of the fundamental dynamics has to be broken. Thereby one gets two representations, one satisfying the generally accepted rules of retarded causality (“no (...)
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