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  1. The Logic of Decision.Richard C. Jeffrey - 1965 - New York, NY, USA: University of Chicago Press.
    "[This book] proposes new foundations for the Bayesian principle of rational action, and goes on to develop a new logic of desirability and probabtility."—Frederic Schick, _Journal of Philosophy_.
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  • (4 other versions)The Logic of Scientific Discovery.Karl Popper - 1959 - Studia Logica 9:262-265.
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  • (4 other versions)The Logic of Scientific Discovery.K. Popper - 1959 - British Journal for the Philosophy of Science 10 (37):55-57.
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  • Probability Theory. The Logic of Science.Edwin T. Jaynes - 2002 - Cambridge University Press: Cambridge. Edited by G. Larry Bretthorst.
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  • What conditional probability could not be.Alan Hájek - 2003 - Synthese 137 (3):273--323.
    Kolmogorov''s axiomatization of probability includes the familiarratio formula for conditional probability: 0).$$ " align="middle" border="0">.
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  • Probability, Induction and Statistics: The Art of Guessing.Bruno De Finetti - 1972 - New York: John Wiley.
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  • (1 other version)Two autonomous axiom systems for the calculus of probabilities.Karl R. Popper - 1955 - British Journal for the Philosophy of Science 6 (21):51-57.
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  • Representational of conditional probabilities.Bas C. Van Fraassen - 1976 - Journal of Philosophical Logic 5 (3):417-430.
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  • You can’t always get what you want: Some considerations regarding conditional probabilities.Wayne C. Myrvold - 2015 - Erkenntnis 80 (3):573-603.
    The standard treatment of conditional probability leaves conditional probability undefined when the conditioning proposition has zero probability. Nonetheless, some find the option of extending the scope of conditional probability to include zero-probability conditions attractive or even compelling. This article reviews some of the pitfalls associated with this move, and concludes that, for the most part, probabilities conditional on zero-probability propositions are more trouble than they are worth.
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  • Finite additivity, another lottery paradox and conditionalisation.Colin Howson - 2014 - Synthese 191 (5):1-24.
    In this paper I argue that de Finetti provided compelling reasons for rejecting countable additivity. It is ironical therefore that the main argument advanced by Bayesians against following his recommendation is based on the consistency criterion, coherence, he himself developed. I will show that this argument is mistaken. Nevertheless, there remain some counter-intuitive consequences of rejecting countable additivity, and one in particular has all the appearances of a full-blown paradox. I will end by arguing that in fact it is no (...)
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  • Rational belief change, Popper functions and counterfactuals.William L. Harper - 1975 - Synthese 30 (1-2):221 - 262.
    This paper uses Popper's treatment of probability and an epistemic constraint on probability assignments to conditionals to extend the Bayesian representation of rational belief so that revision of previously accepted evidence is allowed for. Results of this extension include an epistemic semantics for Lewis' theory of counterfactual conditionals and a representation for one kind of conceptual change.
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  • Some epistemological ramifications of the Borel–Kolmogorov paradox.Michael Rescorla - 2015 - Synthese 192 (3):735-767.
    This paper discusses conditional probability $$P$$ P , or the probability of A given B. When $$P>0$$ P > 0 , the ratio formula determines $$P$$ P . When $$P=0$$ P = 0 , the ratio formula breaks down. The Borel–Kolmogorov paradox suggests that conditional probabilities in such cases are indeterminate or ill-posed. To analyze the paradox, I explore the relation between probability and intensionality. I argue that the paradox is a Frege case, similar to those that arise in many (...)
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  • Conditional Probability in the Light of Qualitative Belief Change.David C. Makinson - 2011 - Journal of Philosophical Logic 40 (2):121 - 153.
    We explore ways in which purely qualitative belief change in the AGM tradition throws light on options in the treatment of conditional probability. First, by helping see why it can be useful to go beyond the ratio rule defining conditional from one-place probability. Second, by clarifying what is at stake in different ways of doing that. Third, by suggesting novel forms of conditional probability corresponding to familiar variants of qualitative belief change, and conversely. Likewise, we explain how recent work on (...)
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  • (1 other version)The British Journal for the Philosophy of Science | Vol 73, No 3.Karl R. Popper - 1955 - British Journal for the Philosophy of Science 6 (24):351-351.
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  • Defusing Bertrand’s Paradox.Zalán Gyenis & Miklós Rédei - 2015 - British Journal for the Philosophy of Science 66 (2):349-373.
    The classical interpretation of probability together with the principle of indifference is formulated in terms of probability measure spaces in which the probability is given by the Haar measure. A notion called labelling invariance is defined in the category of Haar probability spaces; it is shown that labelling invariance is violated, and Bertrand’s paradox is interpreted as the proof of violation of labelling invariance. It is shown that Bangu’s attempt to block the emergence of Bertrand’s paradox by requiring the re-labelling (...)
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  • (1 other version)Two Autonomous Axiom Systems for the Calculus of Probabilities.Karl R. Popper - 1958 - Journal of Symbolic Logic 23 (3):349-349.
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  • A set of independent axioms for probability.Karl R. Popper - 1938 - Mind 47 (186):275-277.
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  • (1 other version)Remarks on the theory of conditional probability: Some issues of finite versus countable additivity.Teddy Seidenfeld - unknown
    This paper (based on joint work with M.J.Schervish and J.B.Kadane) discusses some differences between the received theory of regular conditional distributions, which is the countably additive theory of conditional probability, and a rival theory of conditional probability using the theory of finitely additive probability. The focus of the paper is maximally "improper" conditional probability distributions, where the received theory requires, in effect, that P{a: P(a|a) = 0} = 1. This work builds upon the results of Blackwell and Dubins (1975).
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  • (1 other version)Remarks on the theory of conditional probability: Some issues of finite versus countable additivity.Teddy Seidenfeld - 2001 - In Vincent F. Hendricks, Stig Andur Pedersen & Klaus Frovin Jørgensen (eds.), Probability Theory: Philosophy, Recent History and Relations to Science. Synthese Library, Kluwer.
    This paper discusses some differences between the received theory of regular conditional distributions, which is the countably additive theory of conditional probability, and a rival theory of conditional probability using the theory of finitely additive probability. The focus of the paper is maximally "improper" conditional probability distributions, where the received theory requires, in effect, that P{a: P = 0} = 1. This work builds upon the results of Blackwell and Dubins.
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  • Elements of the theory of probability.Emile Borel - 1909 - Prentice-Hall.
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  • Relative coarse-graining.Jean-Paul Marchand - 1977 - Foundations of Physics 7 (1-2):35-49.
    The problem of statistical inference based on a partial measurement (“coarse-graining”) requires the specification of an a priori distribution. We reformulate the ordinary theory such as to encompass systematically a wide range of a priori distributions (“relative coarse-graining”). This is done in a mathematical setting which admits an interpretation in both classical probability and quantum mechanics. The formalism is illustrated in a few simple examples, such as the die whose geometrical shape is known, the spin in thermal equilibrium with an (...)
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  • Why Bertrand's Paradox is not paradoxical but is felt so.Zalán Gyenis & Miklós Rédei - 2015 - In Uskali Mäki, Stéphanie Ruphy, Gerhard Schurz & Ioannis Votsis (eds.), Recent Developments in the Philosophy of Science. Cham: Springer.
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  • General properties of general Bayesian learning.Miklós Rédei & Zalán Gyenis - unknown
    We investigate the general properties of general Bayesian learning, where ``general Bayesian learning'' means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the inference. States are linear functionals that encode probability measures by assigning expectation values to random variables via integrating them with respect to the probability measure. If (...)
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  • (1 other version)Éléments de la Théorie des Probabilités.Em Borel - 1909 - Revue de Métaphysique et de Morale 17 (6):7-7.
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