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  1. Bayesian Philosophy of Science: Variations on a Theme by the Reverend Thomas Bayes.Jan Sprenger & Stephen Hartmann - 2019 - Oxford and New York: Oxford University Press.
    Jan Sprenger and Stephan Hartmann offer a fresh approach to central topics in philosophy of science, including causation, explanation, evidence, and scientific models. Their Bayesian approach uses the concept of degrees of belief to explain and to elucidate manifold aspects of scientific reasoning.
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  • Kolmogorov Conditionalization, A New Argument For.Michael Nielsen - forthcoming - Review of Symbolic Logic:1-16.
    This paper contributes to a recent research program that extends arguments supporting elementary conditionalization to arguments supporting conditionalization with general, measure-theoretic conditional probabilities. I begin by suggesting an amendment to the framework that Rescorla (2018) has used to characterize regular conditional probabilities in terms of avoiding Dutch book. If we wish to model learning scenarios in which an agent gains complete membership knowledge about some subcollection of the events of interest to her, then we should focus on updating policies that (...)
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  • Kolmogorov Conditionalizers Can Be Dutch Booked.Alexander Meehan & Snow Zhang - forthcoming - Review of Symbolic Logic:1-36.
    A vexing question in Bayesian epistemology is how an agent should update on evidence which she assigned zero prior credence. Some theorists have suggested that, in such cases, the agent should update by Kolmogorov conditionalization, a norm based on Kolmogorov’s theory of regular conditional distributions. However, it turns out that in some situations, a Kolmogorov conditionalizer will plan to always assign a posterior credence of zero to the evidence she learns. Intuitively, such a plan is irrational and easily Dutch bookable. (...)
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  • You Say You Want a Revolution: Two Notions of Probabilistic Independence.Alexander Meehan - 2021 - Philosophical Studies 178 (10):3319-3351.
    Branden Fitelson and Alan Hájek have suggested that it is finally time for a “revolution” in which we jettison Kolmogorov’s axiomatization of probability, and move to an alternative like Popper’s. According to these authors, not only did Kolmogorov fail to give an adequate analysis of conditional probability, he also failed to give an adequate account of another central notion in probability theory: probabilistic independence. This paper defends Kolmogorov, with a focus on this independence charge. I show that Kolmogorov’s sophisticated theory (...)
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  • Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
    Why are conditional degrees of belief in an observation E, given a statistical hypothesis H, aligned with the objective probabilities expressed by H? After showing that standard replies are not satisfactory, I develop a suppositional analysis of conditional degree of belief, transferring Ramsey’s classical proposal to statistical inference. The analysis saves the alignment, explains the role of chance-credence coordination, and rebuts the charge of arbitrary assessment of evidence in Bayesian inference. Finally, I explore the implications of this analysis for Bayesian (...)
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  • Having a Look at the Bayes Blind Spot.Miklós Rédei & Zalán Gyenis - 2019 - Synthese 198 (4):3801-3832.
    The Bayes Blind Spot of a Bayesian Agent is, by definition, the set of probability measures on a Boolean σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document}-algebra that are absolutely continuous with respect to the background probability measure of a Bayesian Agent on the algebra and which the Bayesian Agent cannot learn by a single conditionalization no matter what evidence he has about the elements in the Boolean σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma (...)
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  • The Maxim of Probabilism, with special regard to Reichenbach.Miklós Rédei & Zalán Gyenis - forthcoming - Synthese:1-18.
    It is shown that by realizing the isomorphism features of the frequency and geometric interpretations of probability, Reichenbach comes very close to the idea of identifying mathematical probability theory with measure theory in his 1949 work on foundations of probability. Some general features of Reichenbach’s axiomatization of probability theory are pointed out as likely obstacles that prevented him making this conceptual move. The role of isomorphisms of Kolmogorovian probability measure spaces is specified in what we call the “Maxim of Probabilism”, (...)
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  • Jeffrey Meets Kolmogorov: A General Theory of Conditioning.Alexander Meehan & Snow Zhang - 2020 - Journal of Philosophical Logic 49 (5):941-979.
    Jeffrey conditionalization is a rule for updating degrees of belief in light of uncertain evidence. It is usually assumed that the partitions involved in Jeffrey conditionalization are finite and only contain positive-credence elements. But there are interesting examples, involving continuous quantities, in which this is not the case. Q1 Can Jeffrey conditionalization be generalized to accommodate continuous cases? Meanwhile, several authors, such as Kenny Easwaran and Michael Rescorla, have been interested in Kolmogorov’s theory of regular conditional distributions as a possible (...)
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  • Interpretive Implications of the Sample Space.Dan D. November - unknown
    In this paper I claim that Kolmogorov's probability theory has other basic notions in addition to 'probability' and 'event'. These notions are described by the sample space component of his probability space structure. This claim has several interesting consequences, two of which I discuss in this paper. The major consequence is that the main interpretations of probability theory are in fact not interpretations of Kolmogorov's theory, simply because an interpretation of a mathematical theory in a strict sense must explicate all (...)
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  • A Dutch Book Theorem and Converse Dutch Book Theorem for Kolmogorov Conditionalization.Michael Rescorla - unknown
    This paper discusses how to update one’s credences based on evidence that has initial probability 0. I advance a diachronic norm, Kolmogorov Conditionalization, that governs credal reallocation in many such learning scenarios. The norm is based upon Kolmogorov’s theory of conditional probability. I prove a Dutch book theorem and converse Dutch book theorem for Kolmogorov Conditionalization. The two theorems establish Kolmogorov Conditionalization as the unique credal reallocation rule that avoids a sure loss in the relevant learning scenarios.
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  • Full & Partial Belief.Konstantin Genin - 2019 - In Richard Pettigrew & Jonathan Weisberg (eds.), The Open Handbook of Formal Epistemology. PhilPapers Foundation. pp. 437-498.
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  • Conditional Probabilities.Kenny Easwaran - 2019 - In Richard Pettigrew & Jonathan Weisberg (eds.), The Open Handbook of Formal Epistemology. PhilPapers Foundation. pp. 131-198.
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  • General Properties of Bayesian Learning as Statistical Inference Determined by Conditional Expectations.Zalán Gyenis & Miklós Rédei - 2017 - Review of Symbolic Logic 10 (4):719-755.
    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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  • A Dutch Book Theorem and Converse Dutch Book Theorem for Kolmogorov Conditionalization.Michael Rescorla - 2018 - Review of Symbolic Logic 11 (4):705-735.